computational.science

computational.science@lists.iccsa.org

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two research grants available within the RIPARTI regional projects
by Massimo Cafaro 15 Jun '22

15 Jun '22

The first research grant shall be developed in close collaboration with Planetek Italia (12 months out of 18 in total), and is related to the following topic: Machine Learning for Space Weather The proposed research project is concerned with the study of "Space Weather Phenomena" and the development of knowledge about the mechanisms and effects of solar-derived perturbative phenomena developing in circumterrestrial space and impacting the ionized atmosphere (ionosphere). In the project emphasis is given to the study and modeling of the dynamics of the ionospheric plasma and the electron density irregularities in it on a global scale, in order to improve the capability of long-term (24-48 hours in advance) nowcasting and forecasting of the ionospheric response to Space Weather events over the Mediterranean area. The modeling approach is developed through innovative "machine learning" techniques, recently introduced (Cesaroni et al 2020), the results of which point to this as a strategy to extend the time horizon of ionospheric forecasting, a fundamental requirement for increasing knowledge of Space Weather phenomena in near-Earth space. In addition, the growing demand for semi-empirical approaches for real-time mitigation of errors introduced by the ionosphere on positioning and navigation systems makes the proposed topic a significant contribution in the area of "services and research for society" in relation to the strategic objective "Development of a National Space Weather Service" in the context of developing countermeasures to contain the negative effect that the irregular and disturbed ionosphere can have on technological systems in use in modern society such as, for example, navigation and positioning satellite systems (GNSS, GLobal Navigation Satellite Systems), trans-horizon HF radio communications, and L-band satellite communication systems. Such systems are of interest to a variety of end users who can be identified as users of the service in which the developed products may be embedded. Examples of users may include: precision agriculture operators, operators in the field of mapping, aviation, and radio communications operators for emergency management in civil defense. Cesaroni, C., Spogli, L., Aragon-Angel, A., Fiocca, M., Dear, V., De Franceschi, G., & Romano, V. (2020). Neural network based model for global Total Electron Content forecasting. Journal of Space Weather and Space Climate, 10, 11. The second research grant shall be developed in close collaboration with GE Avio (12 months out of 18 in total), and is related too the following topic: Operative Framework For HPC (Off-HPC) High-performance computing (HPC) clouds are becoming a complement or, in some cases, an alternative to on-premise clusters for running scientific-technical, engineering, and business analytics service applications. Most research efforts in the area of cloud HPC aim to analyze and understand the cost-benefit of migrating computationally intensive applications from on-premise environments to public cloud platforms. Industry trends show that on-premise/cloud hybrid environments are the natural path to get the best out of on-premise and cloud resources. Workloads that are stable from the point of view of required computing resources and sensitive from the point of view of the need to protect processed information can be performed on on-premise resources, while peak computational loads can take advantage of remote computing resources available in the cloud typically under a "pay-as-you-go" consumption mode. The main difficulties in using cloud solutions to run HPC applications stem from their characteristics and properties compared to traditional cloud services to handle, for example, standard enterprise applications, Web applications, data storage or backup, or business intelligence. HPC applications tend to require more computing power than application services typically delivered in cloud environments. These processing requirements arise not only from the characteristics of the CPUs (Central Processing Units), but also from the amount of memory and network speed to support their proper execution. In addition, such applications may have a particular and different execution mechanism than dedicated cloud application services that instead run 24/7. HPC applications tend to run in batch mode. Users execute a series of computational jobs, consisting of instances of the application with different inputs, and wait until results are generated to decide whether new computational tasks need to be submitted and executed. Therefore, moving HPC applications to cloud platforms requires not only a focus on resource allocation in the infrastructure in use and its optimization, but also on how users interact with this new environment. Research in the area of cloud HPC can be classified into three broad categories: (i) feasibility studies on adopting the cloud to replace or complement on-premise computing clusters to run HPC applications; (ii) performance optimization of cloud resources for running HPC applications; and (iii) services to simplify the use of cloud HPC, particularly for users who are not specialized in data and information processing and processing technologies. This research project intends to focus on study activities within the first category, in which, more specifically, there are four main aspects that should be considered: (i) metrics used to assess how feasible the use of HPC cloud is; (ii) resources used in computational experiments; (iii) computational infrastructure; and (iv) software, which includes both well-known HPC benchmarks and computational tools, algorithms, or methodologies related to specific business application cases. Currently, the company uses HPC applications running mostly on on-premise systems but faces issues related to the need for greater computational resources that can be met through flexible and scalable architectures provided by cloud technologies. The need is to build clear technology and governance references for cloud or hybrid infrastructures. The research project will therefore aim to carefully analyze the state of the art of hybrid HPC solutions, define criteria for benchmarking different solutions, develop an operational framework that includes the operational and economic management aspects of a hybrid HPC solution, and finally implement one or more industrial pilots. DEADLINE: June 24, 2022 ALL INCLUSIVE GROSS AMOUNT (for 18 months): 29050,50 euro (i.e., 19367 euro annual gross amount) NOTE: Foreign candidates are strongly encouraged to contact me by email if they need help/support in order to prepare their application: I will be glad to assist. Here you can download an unofficial English translation of the call: RIPARTI-call ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Head of HPC Lab https://hpc-lab.unisalento.it Director of Master in Applied Data Science Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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Expression of interest PhD Positions in Edge Computing
by Carlo Vallati 03 Jun '22

03 Jun '22

Dear all, we are looking for bright and highly motivated student for one PhD position at the Department of Information Engineering at the University of Pisa. The position is funded within the framework of the "Crosslab: Innovation for Industry 4.0" project. The research activities will be carried out in the "Cloud Computing, Big Data & Cybersecurity" laboratory ( https://crosslab.dii.unipi.it/cloud-computing-big-data-cybersecurity-lab). A short description of the research topic can be found below. Interested people are requested to send an expression of interest by submitting a curriculum vitae, a one-page research statement showing motivation and understanding of the topic of the position, and the official Transcript of Record. The expression of interest must be sent by email to Carlo Vallati at carlo.vallati(a)unipi.it with the reference [PhD expression of interest] in the subject of the email. Applications will be reviewed continuously until 5th July 2022. The starting date of the PhD position is Fall 2022. The duration of the PhD is three years. The compensation is a standard Italian Ph.D. student fare, about 1150 Euro/month net. ================================================ Edge Computing 2.0: Efficient Deep Learning at the Edge ================================================ Abstract: Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing, and speech recognition. However, their superior performance comes at the considerable cost of computational complexity, which greatly hinders their applications in many resource-constrained devices, such as Edge computing nodes and Internet of Things (IoT) devices. Therefore, methods and techniques that can lift the efficiency bottleneck while preserving the high accuracy of DNNs are in great demand to enable numerous edge AI applications. The proposed research plan involves the analysis and identification of the challenges related to DNNs for time series prediction both at training time, on the GPU-enabled resource-constrained devices, and at inference time, on microcontrollers, leveraging available open-source software such as Tensorflow and Pytorch. The final goal of the research activity will be the definition, design, implementation, and testing of novel algorithms to improve the efficiency of DNNs on the edge and on IoT devices, on real-case scenarios. Reference contact: Carlo Vallati, email: carlo.vallati(a)unipi.it -- -- ------------------------------ ------- Carlo Vallati, PhD Associate Professor Computer Networking Group Department of Information Engineering University of Pisa Via Diotisalvi 2, 56122 Pisa - Italy Ph. : (+39) 050-2217.572 (direct) .599 (switch) Fax : (+39) 050-2217.600 Skype: warner83 E-mail: carlo.vallati@iet.unipi.ithttp://www.iet.unipi.it/c.vallati/ -----------------------------------------------

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Job Openings: Professor / Associate Professor / Assistant Professor (Computer Science) (3 vacancies) at Hong Kong Baptist University
by COMP HKBU 31 May '22

31 May '22

The Department of Computer Science at Hong Kong Baptist University offers BSc, MSc, MPhil, and PhD programmes, and is now seeking outstanding applicants for the following faculty positions on tenure-track. *Professor / Associate Professor / Assistant Professor (Computer Science) (3 vacancies) (PR0395/21-22)* The appointees will perform high-impact research, teach and supervise students at undergraduate and postgraduate levels, and contribute to professional and institutional services. Collaboration with other faculty members in research and teaching is also expected. They will be encouraged to collaborate with colleagues within the Department to contribute to two special thematic applications including (a) health informatics and (b) security and privacy-aware computing, and to pursue new strategic research initiatives under the Department/Faculty/University. Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct high quality research in areas including but not limited to: Internet of things, Cyber-security, High-performance computing, Big data analytics, Art technologies and Financial technologies. Applicants should also show strong commitment to undergraduate and postgraduate teaching in computer science and/or information systems, possess track record of innovative research and high-impact publications, and be able to bid for and pursue externally-funded research programmes. Initial appointment will be offered on a fixed-term contract of three years. Re-appointment thereafter will be subject to mutual agreement. For enquiries, please contact Professor Jianliang Xu, Head of Department (email: xujl(a)comp.hkbu.edu.hk). More information about the Department can be found at https://www.comp.hkbu.edu.hk. *Rank and salary will be commensurate with qualifications and experience.* *Application Procedure:* Applicants are invited to submit their applications at the HKBU e-Recruitment System (jobs.hkbu.edu.hk) with samples of publications, preferably three best ones out of their most recent publications/works. They should also request two referees to send in confidential letters of reference, with *PR *number (stated above) quoted on the letters, to the Human Resources Office (Email: recruit(a)hkbu.edu.hk) direct. Those who are not invited for interview 4 months after the closing date may consider their applications unsuccessful. All application materials including publication samples, scholarly/creative works will be disposed of after completion of the recruitment exercise. Details of the University’s Personal Information Collection Statement can be found at http://hro.hkbu.edu.hk/pics. The University reserves the right not to make an appointment for the posts advertised, and the appointment will be made according to the terms and conditions applicable at the time of offer. *Closing date: Shortlisting will start immediately until the position is filled.* *URL: *https://www.comp.hkbu.edu.hk/v1/?page=job_vacancies&id=743

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Ph.D. position: Deep learning for estimating the impact of drones on a mobile network
by Tayeb Lemlouma 19 May '22

19 May '22

>> Apologies for cross-posting << *Ph.D. positions at Orange and IRISA Labs (Lannion, France):* *Deep learning for estimating the impact of drones on a mobile network* *Summary* The objective of the thesis is to model and evaluate the impact of connected drone trajectories on a radio access network. This should consider road axes and cellular coverage of operators. The purpose of the model should consider planned drone routes to (1) minimize the impact in terms of traffic on RANs (radio access network), (2) avoid interference between drones and sensitive areas, and (3) maintain good QoE for the user equipments (UEs). *Required competencies:* knowledge of mobile networks, Deep Learning (ideally PyTorch or Tensorflow), Graph theory, Python, shell, Matlab, good communication in English, team work curiosity & open-mindedness. *How to apply?* https://orange.jobs/jobs/offer.do?joid=112419&lang=EN <https://orange.jobs/jobs/offer.do?joid=112419&lang=EN> *More details:* In English: details <https://orange.jobs/jobs/offer.do?joid=112419&lang=EN> In French: details <https://orange.jobs/jobs/offer.do?joid=112418&lang=FR> Please forward to anyone who may be interested. Thanks. >> Apologies for cross-posting <<

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[PSD 2022, MAY 22, Hard Deadline] PRIVACY IN STATISTICAL DATABASES, Springer LNCS, Paris, France, Sept. 2022
by Joaquin Garcia-Alfaro 19 May '22

19 May '22

** Apologies for Cross-postings -- please share ** PRIVACY IN STATISTICAL DATABASES 2022 (PSD 2022) ============================================ Paris, France, September 21-23, 2022 https://crises-deim.urv.cat/psd2022/ Submission deadline: *MAY 22, 2022* (Hard deadline. It has already been extended one week). Submission link: https://easychair.org/conferences/?conf=psd2022 Follow our twitter account to receive the latest news: @PSD_Conference 1. AIMS AND GOAL ----------------- Privacy in statistical databases is about finding tradeoffs to the tension between the increasing societal and economical demand for accurate information and the legal and ethical obligation to protect the privacy of individuals and enterprise which are the respondents providing the statistical data. In the case of statistical databases, the motivation for respondent privacy is one of survival: data collectors cannot expect to collect accurate information from individual or corporate respondents unless these feel the privacy of their responses is guaranteed. Beyond respondent privacy, there are two additional privacy dimensions to be considered: privacy for the data owners (organizations owning or gathering the data, who would not like to share the data they have collected at great expense) and privacy for the users (those who submit queries to the database and would like their analyses to stay private). "Privacy in Statistical Databases 2022" (PSD 2022) is a conference with proceedings published by Springer-Verlag in Lecture Notes in Computer Science. The purpose of PSD 2022 is to attract world-wide, high-level research in statistical database privacy. PSD 2022 is a successor to PSD 2020 (online, Sep. 23-25, 2020, https://unescoprivacychair.urv.cat/psd2020/), PSD 2018 (Valencia, Sep. 26-28, 2018, https://unescoprivacychair.urv.cat/psd2018/), PSD 2016 (Dubrovnik, Sep. 14-16, 2016, https://unescoprivacychair.urv.cat/psd2016/), PSD 2014 (Eivissa, Sep. 17-19, 2014, http://unescoprivacychair.urv.cat/psd2014/), PSD 2012 (Palermo, Sep. 26-28, 2012, http://unescoprivacychair.urv.cat/psd2012), PSD 2010 (Corfu, Sep. 22-24, 2010, http://unescoprivacychair.urv.cat/psd2010), PSD 2008 (Istanbul, Sep. 24-26, 2008, http://unescoprivacychair.urv.cat/psd2008), PSD 2006 (Rome, Dec. 13-15, 2006, http://crises-deim.urv.cat/psd2006) and PSD 2004 (Barcelona, June 9-11, 2004, http://crises-deim.urv.cat/psd2004), all with proceedings published by Springer in LNCS 12276, LNCS 11126, LNCS 9867, LNCS 8744, LNCS 7556, LNCS 6344, LNCS 5262, LNCS 4302 and LNCS 3050, respectively. Those ten PSD conferences follow a tradition of high-quality technical conferences on SDC which started with "Statistical Data Protection-SDP'98", held in Lisbon in 1998 and with proceedings published by OPOCE, and continued with the AMRADS project SDC Workshop, held in Luxemburg in 2001 and with proceedings published in Springer LNCS 2316. Like the aforementioned preceding conferences, PSD 2022 originates in Europe, but wishes to stay a worldwide event in database privacy and SDC. Thus, contributions and attendees from overseas are welcome. 2. ORGANIZATION --------------- PROGRAM COMMITTEE - Jane Bambauer (University of Arizona, USA) - Bettina Berendt (Technical University of Berlin, Germany) - Aleksandra Bujnowska (EUROSTAT, European Union) - Jordi Castro (Polytechnical University of Catalonia) - Anne-Sophie Charest (Universite Laval, Quebec, Canada) - Peter Christen (Australian National University, Australia) - Christopher Clifton (Purdue University, USA) - Graham Cormode (University of Warwick, UK) - Roberto DiPietro (Hamad Bin Khalifa University, Qatar) - Josep Domingo-Ferrer (Universitat Rovira i Virgili, Catalonia) - Joerg Drechsler (IAB, Germany) - Khaled El Emam (University of Ottawa, Canada) - Mark Elliot (Manchester University, UK) - Sebastien Gambs (Universite du Quebec a Montreal) - Sarah Giessing (Destatis, Germany) - Marc Juarez (University of Southern California, USC) - Hiroaki Kikuchi (Meiji University, Japan) - Maryline Laurent (Telecom SudParis-Institut Polytechnique de Paris, France) - Bradley Malin (Vanderbilt University, USA) - Anna Monreale (Universita di Pisa, Italy) - Krish Muralidhar (The University of Oklahoma, USA) - David Naccache (Ecole Normale Superieure, France) - Benjamin Nguyen (INSA Centre Val de Loire, France) - Anna Oganyan (National Center for Health Statistics, USA) - Melek Onen (Eurecom, France) - Constantinos Patsakis (University of Piraeus, Greece) - Jerry Reiter (Duke University, USA) - Yosef Rinott (Hebrew University, Israel) - Salvatore Ruggieri (Universita di Pisa, Italy) - Steven Ruggles (University of Minnesota, USA) - Nicolas Ruiz (OECD) - Pierangela Samarati (University of Milan, Italy) - David Sanchez (Universitat Rovira i Virgili, Catalonia) - Monica Scannapieco (ISTAT, Italy) - Eric Schulte Nordholt (Statistics Netherlands) - Natalie Shlomo (University of Manchester, UK) - Aleksandra Slavkovic (Penn State University, USA) - Jordi Soria-Comas (Catalan Data Protection Authority, Catalonia) - Tamir Tassa (The Open University, Israel) - Vicenc Torra (Umea University, Sweden) - Rolando Trujillo-Rasua (Deakin University, Australia) - Lars Vilhuber (Cornell University, USA) - Peter-Paul de Wolf (Statistics Netherlands) PROGRAM CHAIR - Josep Domingo-Ferrer (Universitat Rovira i Virgili, Catalonia) GENERAL CHAIR - Maryline Laurent (Telecom SudParis-Institut Polytechnique de Paris, France) ORGANIZATION COMMITTEE - Samia Bouzefrane (CNAM, France) - Joaquin Garcia-Alfaro (Telecom SudParis-Institut Polytechnique de Paris, France) - Miriam Guillem (Universitat Rovira i Virgili, Catalonia) - Jesus Manjon (Universitat Rovira i Virgili, Catalonia) 3. TOPICS OF INTEREST --------------------- Topics of interest include but are not limited to: - New anonymization methods for tabular data - New anonymization methods for microdata (including non-conventional microdata types such as trajectories, graphs, etc.) - Best anonymization practices for tabular data - Best anonymization practices for microdata - Co-utility for privacy preservation - Big data anonymization - Streaming data anonymization - Decentralized anonymization - Balancing data quality and data confidentiality in SDC - Differential privacy and other privacy models - SDC transparency issues - Onsite access centers - Remote access facilities - SDC software - Estimating disclosure risk in SDC - Record linkage methods - Real-life disclosure scenarios in EU-member states and abroad - Privacy preserving data mining (both cryptographic and non-cryptographic) - Private information retrieval - Privacy in web-based e-commerce - Privacy in healthcare - Privacy in official and corporate statistics - Other data anonymization issues 4. SUBMISSIONS -------------- Full papers containing either original technical contributions or high-quality surveys on the above topics or on related topics are sought. Camera-ready versions of accepted papers should be prepared using the LaTeX2estyle or the Word template of Springer Verlag Lecture Notes in Computer Science. For LaTeX2e, a macro package llncs.zip and an example file typeinst.zip can be downloaded from https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu…. For Microsoft Word, a template word.zip can be downloaded from the same page above. We encourage authors to use the above formats already for their submissions. LENGTH OF SUBMISSIONS. Using the above format with 11 point font, the paper should be at most 12 pages excluding bibliography and appendices, and at most 16 pages total. Committee members are not required to read appendices; the paper should be intelligible without them. Submissions not meeting these guidelines risk rejection without consideration of their merits. 5. PROCEEDINGS -------------- Among PSD 2022 accepted papers, a selection will be made based on quality and coverage and the selected papers will be published in the Lecture Notes in Computer Science (LNCS) series by Springer. This follows the tradition of the previous PSD conferences. The remaining accepted papers will be published in a USB with an ISBN. It is possible to submit a paper directly for the USB, which benefits from a later submission deadline and no copyright transfer (see USB-only dates below). The form of publication of an accepted paper will be clearly specified in the acceptance message. Both the LNCS volume and the USB will be *available at the conference*. 6. IMPORTANT DATES ------------------ Submission deadline: *May 22, 2022** (Hard deadline. It has already been extended one week) Acceptance notification: June 17, 2022 Proceedings version due: June 26, 2022 USB-only submission deadline: June 26, 2022 USB-only acceptance notification: July 6, 2022 USB-only proceedings version due: July 13, 2022 Conference: Sep. 21-23, 2022 7. VENUE AND TRAVEL ------------------- The conference will take place at the headquarters of the Conservatoire National des Arts et Metiers (CNAM) in central Paris. Further venue, travel and accomodation information will be posted no later than June 2022 at https://crises-deim.urv.cat/psd2022 8. REGISTRATION --------------- Registration information will be posted no later than June 2022 at http://crises-deim.urv.cat/psd2022

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[NEW DEADLINE] ESR 3 years research grant - Marie Skłodowska-Curie Action - InnoCyPES European project
by Massimo Cafaro 18 May '22

18 May '22

A three years research grant is available within the InnocyPES European project. The position is related to ESR4, for research on Large Scale Data Management and Integration DEADLINE: June 2, 2022 Ukranian Researchers are strongly encouraged to apply! Downloadable application forms etc are available: official Italian documents: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 unofficial English translation: http://sara.unisalento.it/~cafaro/page-3/ InnoCyPES announcement: https://innocypes.eu/index.php/2022/03/29/esr-4-large-scale-data-management… Foreign candidates are strongly encouraged to contact Prof. Cafaro by email (massimo.cafaro(a)unisalento.it) if they need help/support in order to prepare their application. A public selection procedure is called for a research grant for collaboration in research activities (hereinafter referred to as research grant), at the Department of Innovation Engineering of the University of Salento. The location, the duration, the amount, the scientific disciplinary sector, the scientific referent, the structure available to the winner and the program of the research grant are specified below: STRUCTURE Department of Engineering for Innovation, University of Salento, Lecce, Italy DURATION 3 years REMUNERATION The research grant will last 3 (three) years. The annual remuneration, gross of charges to be borne by the beneficiary and inclusive of contributions and social security charges to be borne by the University of Salento, consists of the following items: 1. Living allowance: Euro 40,966.56 (forty thousand nine hundred and sixty-six/56); 2. Mobility allowance: Euro 7.200,00 (seven thousand two hundred/00); 3. Family allowance: 3.1.Euro 0 (zero) per researcher without family obligations; 3.2.Euro 6,000.00 (six thousand/00) for researchers with family obligations (married or with a relationship recognised by Italian law or that of the country of origin or with dependent children). SCIENTIFIC SUPERVISOR Prof. Massimo Cafaro RESEARCH TITLE LARGE SCALE DATA MANAGEMENT AND INTEGRATION DESCRIPTION This is a three year “Marie Curie ETN Early Stage Researcher position” on the following topic. The sheer quantum of data being created and collected across jurisdictions requires a carefully planned and proactive approach to data management. The need for fusion and integration of multiple data sources characterized by fragmented data ownership is driving innovative approaches to large scale distributed data management and integration to avoid inconsistent and inaccurate data. The aim is to investigate, design and implement a fully decentralized solution to provide efficient management of dynamically updated information and support for distributed queries. One or more domain specific use cases shall be identified within the context of the project, considering both the current and future needs of some of the involved partners. These nicely fit into the research plan, owing to the need of surveying the user’s requirements to begin with; simultaneously, the uses cases can also be thought of as sources of advanced data management challenges. For admission to the selection is required the possession of all the requirements provided by law for access to public employment, the eligibility requirements provided by the Marie Sklodowska- Curie Action (https://ec.europa.eu/research/participants/data/ref/h2020/wp/2018-2020/main…) and the following requirements: a) Master of Science degree in Computer Science or Computer Engineering or equivalent qualification that formally allows access to a PhD program in Italy; b) an excellent academic career; c) must not have carried out more than 4 years of research activity after obtaining the degree mentioned in the previous point within the expected starting date of the contract (indicatively fixed at 01.03.2022); d) have a background relevant to the following areas but not limited to: distributed computing, databases, distributed data management, security and privacy; knowledge of C/C++ programming languages and time series archiving/analysis is a plus; e) must not have a Ph.D. as of the contract start date; f) must not have resided or carried out the main activity of study/work in Italy for more than 12 months during the 3 years preceding the date of the beginning of the contract; g) have an excellent knowledge of the English language, in any case sufficient to ensure the performance of the activity envisaged by the contract and daily interaction in the working environment; h) must not have criminal convictions or have pending criminal proceedings of particular gravity. Lack of even one of the above requirements will result in exclusion from the selection process at any time. Qualifications obtained abroad must normally be previously recognized in Italy in accordance with current legislation. The equivalence of qualifications obtained abroad that have not already been recognized in Italy will be evaluated by the Selection Committee solely for the purpose of admitting the candidate to this call for selection. The qualifications must be possessed on the date of expiry of the deadline established for the presentation of applications for admission to this selection. The University of Salento guarantees equality and equal opportunities between men and women for the allocation of the grants in question and the protection of confidentiality in the processing of personal data, according to the provisions in force. The position is officially advertised at the University of Unisalento: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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[DEADLINE EXTENDED] 3 Years Marie Skłodowska-Curie ESR research grant - InnocyPES European project.
by Massimo Cafaro 26 Apr '22

26 Apr '22

A three years research grant is available within the InnocyPES European project. The position is related to ESR4, for research on Large Scale Data Management and Integration DEADLINE: extended to May 5, 2022 Ukranian Researchers are strongly encouraged to apply! Downloadable application forms etc are available: official Italian documents: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 unofficial English translation: http://sara.unisalento.it/~cafaro/page-3/ InnoCyPES announcement: https://innocypes.eu/index.php/2022/03/29/esr-4-large-scale-data-management… Foreign candidates are strongly encouraged to contact Prof. Cafaro by email (massimo.cafaro(a)unisalento.it) if they need help/support in order to prepare their application. A public selection procedure is called for a research grant for collaboration in research activities (hereinafter referred to as research grant), at the Department of Innovation Engineering of the University of Salento. The location, the duration, the amount, the scientific disciplinary sector, the scientific referent, the structure available to the winner and the program of the research grant are specified below: STRUCTURE Department of Engineering for Innovation, University of Salento, Lecce, Italy DURATION 3 years REMUNERATION The research grant will last 3 (three) years. The annual remuneration, gross of charges to be borne by the beneficiary and inclusive of contributions and social security charges to be borne by the University of Salento, consists of the following items: 1. Living allowance: Euro 40,966.56 (forty thousand nine hundred and sixty-six/56); 2. Mobility allowance: Euro 7.200,00 (seven thousand two hundred/00); 3. Family allowance: 3.1.Euro 0 (zero) per researcher without family obligations; 3.2.Euro 6,000.00 (six thousand/00) for researchers with family obligations (married or with a relationship recognised by Italian law or that of the country of origin or with dependent children). SCIENTIFIC SUPERVISOR Prof. Massimo Cafaro RESEARCH TITLE LARGE SCALE DATA MANAGEMENT AND INTEGRATION DESCRIPTION This is a three year “Marie Curie ETN Early Stage Researcher position” on the following topic. The sheer quantum of data being created and collected across jurisdictions requires a carefully planned and proactive approach to data management. The need for fusion and integration of multiple data sources characterized by fragmented data ownership is driving innovative approaches to large scale distributed data management and integration to avoid inconsistent and inaccurate data. The aim is to investigate, design and implement a fully decentralized solution to provide efficient management of dynamically updated information and support for distributed queries. One or more domain specific use cases shall be identified within the context of the project, considering both the current and future needs of some of the involved partners. These nicely fit into the research plan, owing to the need of surveying the user’s requirements to begin with; simultaneously, the uses cases can also be thought of as sources of advanced data management challenges. For admission to the selection is required the possession of all the requirements provided by law for access to public employment, the eligibility requirements provided by the Marie Sklodowska- Curie Action (https://ec.europa.eu/research/participants/data/ref/h2020/wp/2018-2020/main…) and the following requirements: a) Master of Science degree in Computer Science or Computer Engineering or equivalent qualification that formally allows access to a PhD program in Italy; b) an excellent academic career; c) must not have carried out more than 4 years of research activity after obtaining the degree mentioned in the previous point within the expected starting date of the contract (indicatively fixed at 01.03.2022); d) have a background relevant to the following areas but not limited to: distributed computing, databases, distributed data management, security and privacy; knowledge of C/C++ programming languages and time series archiving/analysis is a plus; e) must not have a Ph.D. as of the contract start date; f) must not have resided or carried out the main activity of study/work in Italy for more than 12 months during the 3 years preceding the date of the beginning of the contract; g) have an excellent knowledge of the English language, in any case sufficient to ensure the performance of the activity envisaged by the contract and daily interaction in the working environment; h) must not have criminal convictions or have pending criminal proceedings of particular gravity. Lack of even one of the above requirements will result in exclusion from the selection process at any time. Qualifications obtained abroad must normally be previously recognized in Italy in accordance with current legislation. The equivalence of qualifications obtained abroad that have not already been recognized in Italy will be evaluated by the Selection Committee solely for the purpose of admitting the candidate to this call for selection. The qualifications must be possessed on the date of expiry of the deadline established for the presentation of applications for admission to this selection. The University of Salento guarantees equality and equal opportunities between men and women for the allocation of the grants in question and the protection of confidentiality in the processing of personal data, according to the provisions in force. The position is officially advertised at the University of Unisalento: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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CfP: 29th IEEE International Conference on High Performance Computing, Data & Analytics (HiPC 2022)
by Anand Panangadan 17 Apr '22

17 Apr '22

HiPC 2022 29th IEEE International Conference on High Performance Computing, Data & Analytics Dec. 18-21, 2022 Bengaluru, India Website: http://www.hipc.org CALL FOR PAPERS HiPC 2022 will be the 29th edition of the IEEE International Conference on High Performance Computing, Data, Analytics, and Data Science. HiPC serves as a forum to present current work by researchers from around the world as well as highlight activities in Asia in the areas of high performance computing and data science. The meeting focuses on all aspects of high performance computing systems, and data science and analytics, and their scientific, engineering, and commercial applications. Authors are invited to submit original unpublished research manuscripts that demonstrate current research in all areas of high performance computing, and data science and analytics, covering all traditional areas and emerging topics including from machine learning, big data analytics. Each submission should be submitted to one of the six tracks listed under the two broad themes of High Performance Computing and Data Science. Up to two best paper awards will be given for outstanding contributed papers. Depending on how the COVID-19 pandemic situation evolves, the presentation may be in person or in a virtual format. Authors of selected high-quality papers in HiPC 2022 will be invited to submit extended versions of their papers for possible publication in a special issue of the Journal of Parallel and Distributed Computing (JPDC). HIGH PERFORMANCE COMPUTING Topics for papers include, but are not limited to the topics given under the categories below. Algorithms: This track invites papers that describe original research on developing new parallel and distributed computing algorithms, and related advances. Examples of topics that are of interest include (but not limited to): - New parallel and distributed algorithms and design techniques; - Advances in enhancing algorithmic properties or providing guarantees (e.g., concurrency, data locality, communication-avoiding, asynchronous, hybrid CPU-GPU algorithms, fault tolerance, resilience,); - Algorithmic techniques for resource allocation and optimization (e.g., scheduling, load balancing, resource management); - Provably efficient parallel and distributed algorithms for advanced scientific computing and irregular applications (e.g., numerical linear algebra, graph algorithms, computational biology); - Classical and emerging computation models (e.g., parallel/distributed models, quantum computing, neuromorphic and other bioinspired models). Architecture: This track invites papers that describe original research on the design and evaluation of high performance computing architectures, and related advances. Examples of topics of interest include (but not limited to): - High performance processing architectures (e.g., reconfigurable, system-on-chip, many cores, vector processors); - Networks for high performance computing platforms (e.g., interconnect topologies, network-on-chip); - Memory, cache and storage architectures (e.g., 3D, photonic, Processing-In-Memory, NVRAM, burst buffers, parallel I/O); - Approaches to improve architectural properties (e.g., energy/power efficiency, reconfigurable, resilience/fault tolerance, security/privacy); - Emerging computational architectures (e.g., quantum computing, neuromorphic and other bioinspired architectures). Applications: This track invites papers that describe original research on the design and implementation of scalable and high performance applications for execution on parallel, distributed and accelerated platforms, and related advances. Examples of topics of interest include (but not limited to): - Shared and distributed memory parallel applications (e.g., scientific computing, simulation and visualization applications, graph and irregular applications, data-intensive applications, science/engineering/industry applications, emerging applications in IoT and life sciences, etc.); - Methods, algorithms, and optimizations for scaling applications on peta- and exa-scale platforms (e.g., co-design of hardware and software, heterogeneous and hybrid programming); - Hardware acceleration of parallel applications (e.g., GPUs, FPGA, vector processors, manycore); - Application benchmarks and workloads for parallel and distributed platforms. Systems Software: This track invites papers that describe original research on the design, implementation, and evaluation of systems software for high performance computing platforms, and related advances. Examples of topics of interest include (but not limited to): - Scalable systems and software architectures for high-performance computing (e.g., middleware, operating systems, I/O services); - Techniques to enhance parallel performance (e.g., compiler/runtime optimization, learning from application traces, profiling); - Techniques to enhance parallel application development and productivity (e.g., Domain-Specific Languages, programming environments, performance/correctness checking and debugging); - Techniques to deal with uncertainties, hardware/software resilience, and fault tolerance; - Software for cloud, data center, and exascale platforms (e.g., middleware tools, schedulers, resource allocation, data migration, load balancing); - Software and programming paradigms for heterogeneous platforms (e.g., libraries for CPU/GPU, multi-GPU clusters, and other accelerator platforms). SCALABLE DATA SCIENCE Scalable Algorithms and Analytics: This track invites papers that describe original research on developing scalable algorithms for data analysis at scale, and related advances. Examples of topics of interest include (but not limited to): - New scalable algorithms for fundamental data analysis tasks (supervised, unsupervised learning, data (pre-)processing and pattern discovery); - Scalable algorithms that are designed to address the characteristics of different data sources and settings (e.g., graphs, social networks, sequences, data streams); - Scalable algorithms and techniques to reduce the complexity of large-scale data (e.g., streaming, sublinear data structures, summarization, compressive analytics); - Scalable algorithms that are designed to address requirements in different data-driven application domains (e.g., life sciences, business, agriculture, health sciences); - Scalable algorithms that ensure the transparency and fairness of the analysis; - Case studies, experimental studies, and benchmarks for scalable algorithms and analytics; - Scaling and accelerating machine learning, deep learning, natural language processing and computer vision applications. Scalable Systems and Software: This track invites papers that describe original research on developing scalable systems and software for handling data at scale and related advances. Examples of topics of interest include (but not limited to): - New parallel and distributed algorithms and design techniques; - Design of scalable system software to support various applications (e.g., recommendation systems, web search, crowdsourcing applications, streaming applications); - Scalable system software for various architectures (e.g., OpenPower, GPUs, FPGAs); - Architectures and systems software to support various operations in large data frameworks (e.g., storage, retrieval, automated workflows, data organization, visualization, visual analytics, human-in-the-loop); - Systems software for distributed data frameworks (e.g., distributed file system, data deduplication, virtualization, cloud services, resource optimization, scheduling); - Standards and protocols for enhancing various aspects of data analytics (e.g., open data standards, privacy-preserving, and secure schemes). Important dates - Submission site open: June 15, 2022 - Abstract submissions: July 4, 2022 AOE - Full Paper submissions: July 8, 2022 AOE - First-round Author notifications: September 12, 2022 - Submission of revised papers along with response to reviews: October 10, 2022 - Author notification for revised papers: November 1, 2022 - Camera-ready version: November 15, 2022 - Conference dates: December 18-21, 2022 General Co-chairs: - Chiranjib Sur, Shell, India - Neelima Bayyapu, Consultant, India Vice General Co-chairs: - Sanmukh Rao Kuppannagari, University of Southern California, USA - Vivek Yadav, IIIT-Bangalore, India- - Program Co–chairs: - High performance computing: Sathish Vadhiyar, Indian Institute of Science, India - Data science: Jun Wang, University of Central Florida, USA Steering committee chair: - Viktor K. Prasanna, University of Southern California, USA Program Vice-Chairs HPC TRACKS - Algorithms: Thomas Herault, University of Tennessee, USA - Applications: Yogish Sabharwal, IBM IRL, India - Architecture: Diana Goehringer, TU Dresden, Germany - System Software: Jyothi Vedurada, IIT, Hyderabad DATA SCIENCE TRACKS - Scalable Algorithms and Analytics: Zhishan Guo, University of Central Florida, USA - Scalable Systems and Software: Dan Huang, Sun Yat-Sen University, PRC

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Virtualization in High-Performance Cloud Computing (VHPC 2022) - Submission deadline extended
by Tommaso Cucinotta 14 Apr '22

14 Apr '22

[apologies for cross-postings] *The paper submission deadline for VHPC 2022 has been extended to April 26th (AoE). Please, register your abstract by April 19th.* The Workshop on Virtualization in High-Performance Cloud Computing (VHPC) <vhpc.org/> is an international forum bringing together researchers and industrial practitioners facing the challenges posed by virtualization in HPC/Cloud scenarios, in order to foster discussion, collaboration, mutual exchange of knowledge and experience, enabling research to ultimately provide novel solutions for virtualized computing systems of tomorrow. The 17th edition of VHPC will be held on June 2nd, jointly with the ISC High-Performance 2022 <https://www.isc-hpc.com/> conference and exhibition in Hamburg (Germany), and will feature two excellent *industrial **_/keynote speakers/_* * “rtla: finding the sources of OS noise on Linux”, Daniel Bristot De Oliveira, Senior Principal Software Engineer in the real-time kernel team at Red Hat * “DynamoDB: NoSQL database services for predictable HPC workloads”, Akshat Vig, Principal Software Engineer at Amazon Web Services (AWS) In addition to the general research topics mentioned below, VHPC'22 encourages particularly contributions on the following _/focus topics/_: * Container Platforms (Kubernetes, Docker, Singularity, Shifter, rkt, …) for Scientific Workflows * Composable Lightweight Applications and Unikernel Frameworks * Latency Control and Data/Container Placement in Heterogeneous HPC Virtualized Environments * Energy-efficiency and Service Orchestration in Virtualized Cloud & HPC Infrastructures *Workshop Overview* Containers and virtualization technologies constitute key enabling factors for flexible resource management in modern data centers, and particularly in cloud environments. Cloud providers need to manage complex and heterogeneous infrastructures in a seamless fashion to support the highly dynamic and diverse workloads and applications customers deploy. Similarly, HPC environments have been increasingly adopting techniques that enable flexible management of vast computing and networking resources, close to marginal provisioning cost, which is unprecedented in the history of scientific and commercial computing. More recently, Function as a Service (Faas) and Serverless computing, leveraging on lightweight virtualizaton and containerization solutions, widens the spectrum of applications that can be deployed in a cloud environment, especially in an HPC context. Here, HPC-provided services can become accessible to distributed workloads outside of large cluster environments. Various virtualization-containerization technologies contribute to the overall picture in different ways: machine virtualization, with its capability to enable consolidation of multiple underutilized servers with heterogeneous software and operating systems (OSes), and its capability to live-migrate a fully operating virtual machine (VM) with a very short downtime, enables novel and dynamic ways to manage physical servers; OS-level virtualization (i.e., containerization), with its capability to isolate multiple user-space environments and to allow for their coexistence within the same OS kernel, promises to provide many of the advantages of machine virtualization with bare-metal responsiveness and performance; lastly, unikernels provide for many virtualization benefits with a minimized OS/library surface. I/O Virtualization in turn allows physical network interfaces to take traffic from multiple VMs or containers; network virtualization, with its capability to create logical network overlays that are independent of the underlying physical topology is furthermore enabling virtualization of HPC infrastructures. *Topics of Interest* The VHPC program committee solicits original, high-quality submissions related to virtualization across the entire software stack with a special focus on the intersection of HPC, containers-virtualization and cloud computing. Each topic encompasses aspects related to design/architecture, management, performance management, modeling and configuration/tooling: Design / Architecture: * Containers and OS-level virtualization (LXC, Docker, rkt, Singularity, Shifter) * Hypervisor support for heterogeneous resources (GPUs, co-processors, FPGAs, etc.) * Hypervisor extensions to mitigate side-channel attacks ([micro-]architectural timing attacks, privilege escalation) * VM & Container trust and security models * Multi-environment coupling, system software supporting in-situ analysis with HPC simulation * Cloud reliability, fault-tolerance and high-availability * Energy-efficient and power-aware virtualization * Containers inside VMs with hypervisor isolation * Virtualization support for emerging memory technologies * Lightweight/specialized operating systems in conjunction with virtual machines * Hypervisor support for heterogeneous resources (GPUs, co-processors, FPGAs, etc.) * Novel unikernels and use cases for virtualized HPC environments * ARM-based hypervisors, ARM virtualization extensions Management: * Container, VM and data management for HPC and cloud environments * HPC services integration, services to support HPC * Service and on-demand scheduling & resource management * Dedicated workload management with VMs or containers * Workflow coupling with VMs and containers * Unikernels and lightweight VM application management * Environments and tools for operating containerized environments (batch, orchestration) * Novel models for non-HPC workload provisioning on HPC resources Performance Measurements and Modeling: * Performance improvements for or driven by unikernels * Optimizations of virtual machine monitor platforms and hypervisors * Scalability analysis of VMs and/or containers at large scale * Performance measurement, modeling and monitoring of virtualized/cloud workloads * Virtualization in supercomputing environments, HPC clusters, HPC in the cloud * Energy-efficient deployment of high-performance, ultra-low latency and real-time workloads in cloud infrastructures * Modeling, control and isolation of end-to-end performance for parallel & distributed cloud/HPC applications Configuration / Tooling: * Tool support for unikernels: configuration/build environments, debuggers, profilers * Job scheduling/control/policy and container placement in virtualized environments * Measuring and controlling “OS/Virtualization noise” * Operating MPI in containers/VMs and Unikernels * GPU virtualization operationalization The workshop will be one day in length, composed of 20 min paper presentations, each followed by 10 min discussion sections, plus lightning talks that are limited to 5 minutes. Presentations may be accompanied by interactive demonstrations. For more information and detailed paper submission instructions, refer to the VHPC'22 webpage <https://vhpc.org/>: https://vhpc.org/ *Important Dates* * *Apr 19th, 2022 (extended)*: Abstract submission (opens /Feb 14th, 2022/) * *Apr 26th, 2022 (extended)*: Paper submission deadline (Springer LNCS) * *May 6th, 2022*: Acceptance notification * *Jun 2nd, 2022*: Workshop Day * *Jul 10th, 2022*: Camera-ready version due (post-workshop) *General Chairs* * Michael Alexander, BOKU Vienna, Austria * Anastassios Nanos, Nubificus Ltd., UK * Tommaso Cucinotta, Scuola Superiore Sant’Anna, Ital -- Tommaso Cucinotta, Associate Professor of Computer Engineering, PhD Head of the Real-Time Systems Laboratory (ReTiS) Scuola Superiore Sant'Anna, Pisa, Italy http://retis.sssup.it/~tommaso/eng/research.html

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[DEADLINE APPROACHING] InnoCyPES European Project - ESR4 position available
by Massimo Cafaro 10 Apr '22

10 Apr '22

A three years research grant is available within the InnocyPES European project. The position is related to ESR4, for research on Large Scale Data Management and Integration DEADLINE: April 19, 2022 Ukranian Researchers are strongly encouraged to apply! Downloadable application forms etc are available: official Italian documents: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 unofficial English translation: http://sara.unisalento.it/~cafaro/page-3/ InnoCyPES announcement: https://innocypes.eu/index.php/2022/03/29/esr-4-large-scale-data-management… Foreign candidates are strongly encouraged to contact Prof. Cafaro by email (massimo.cafaro(a)unisalento.it) if they need help/support in order to prepare their application. A public selection procedure is called for a research grant for collaboration in research activities (hereinafter referred to as research grant), at the Department of Innovation Engineering of the University of Salento. The location, the duration, the amount, the scientific disciplinary sector, the scientific referent, the structure available to the winner and the program of the research grant are specified below: STRUCTURE Department of Engineering for Innovation, University of Salento, Lecce, Italy DURATION 3 years REMUNERATION The research grant will last 3 (three) years. The annual remuneration, gross of charges to be borne by the beneficiary and inclusive of contributions and social security charges to be borne by the University of Salento, consists of the following items: 1. Living allowance: Euro 40,966.56 (forty thousand nine hundred and sixty-six/56); 2. Mobility allowance: Euro 7.200,00 (seven thousand two hundred/00); 3. Family allowance: 3.1.Euro 0 (zero) per researcher without family obligations; 3.2.Euro 6,000.00 (six thousand/00) for researchers with family obligations (married or with a relationship recognised by Italian law or that of the country of origin or with dependent children). SCIENTIFIC SUPERVISOR Prof. Massimo Cafaro RESEARCH TITLE LARGE SCALE DATA MANAGEMENT AND INTEGRATION DESCRIPTION This is a three year “Marie Curie ETN Early Stage Researcher position” on the following topic. The sheer quantum of data being created and collected across jurisdictions requires a carefully planned and proactive approach to data management. The need for fusion and integration of multiple data sources characterized by fragmented data ownership is driving innovative approaches to large scale distributed data management and integration to avoid inconsistent and inaccurate data. The aim is to investigate, design and implement a fully decentralized solution to provide efficient management of dynamically updated information and support for distributed queries. One or more domain specific use cases shall be identified within the context of the project, considering both the current and future needs of some of the involved partners. These nicely fit into the research plan, owing to the need of surveying the user’s requirements to begin with; simultaneously, the uses cases can also be thought of as sources of advanced data management challenges. For admission to the selection is required the possession of all the requirements provided by law for access to public employment, the eligibility requirements provided by the Marie Sklodowska- Curie Action (https://ec.europa.eu/research/participants/data/ref/h2020/wp/2018-2020/main…) and the following requirements: a) Master of Science degree in Computer Science or Computer Engineering or equivalent qualification that formally allows access to a PhD program in Italy; b) an excellent academic career; c) must not have carried out more than 4 years of research activity after obtaining the degree mentioned in the previous point within the expected starting date of the contract (indicatively fixed at 01.03.2022); d) have a background relevant to the following areas but not limited to: distributed computing, databases, distributed data management, security and privacy; knowledge of C/C++ programming languages and time series archiving/analysis is a plus; e) must not have a Ph.D. as of the contract start date; f) must not have resided or carried out the main activity of study/work in Italy for more than 12 months during the 3 years preceding the date of the beginning of the contract; g) have an excellent knowledge of the English language, in any case sufficient to ensure the performance of the activity envisaged by the contract and daily interaction in the working environment; h) must not have criminal convictions or have pending criminal proceedings of particular gravity. Lack of even one of the above requirements will result in exclusion from the selection process at any time. Qualifications obtained abroad must normally be previously recognized in Italy in accordance with current legislation. The equivalence of qualifications obtained abroad that have not already been recognized in Italy will be evaluated by the Selection Committee solely for the purpose of admitting the candidate to this call for selection. The qualifications must be possessed on the date of expiry of the deadline established for the presentation of applications for admission to this selection. The University of Salento guarantees equality and equal opportunities between men and women for the allocation of the grants in question and the protection of confidentiality in the processing of personal data, according to the provisions in force. The position is officially advertised at the University of Unisalento: https://www.unisalento.it/bandi-concorsi/-/bandi/view/66046831 ******************************************************************************************* Prof. Massimo Cafaro, Ph.D. Associate Professor of Parallel Algorithms and Data Mining/Machine Learning Department of Engineering for Innovation University of Salento, Lecce, Italy Via per Monteroni 73100 Lecce, Italy Voice/Fax +39 0832 297371 Web http://sara.unisalento.it/~cafaro Web https://www.unisalento.it/people/massimo.cafaro E-mail massimo.cafaro(a)unisalento.it E-mail cafaro(a)ieee.org E-mail cafaro(a)acm.org INGV National Institute of Geophysics and Volcanology Via di Vigna Murata 605 Roma CMCC Foundation Euro-Mediterranean Center on Climate Change Via Augusto Imperatore, 16 - 73100 Lecce massimo.cafaro(a)cmcc.it ******************************************************************************************* --

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