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CFP: IEEE ISPA2022 (Parallel and Distributed Processing with Applications), Dec. 2022, Melbourne, Australia
by Jesson Butt 22 Aug '22

22 Aug '22

Call for papers: The 20th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2022), 17-19 Dec. 2022, Melbourne, Australia. Website: http://www.swinflow.org/confs/2022/ispa/ Key dates: Submission Deadline: September 25, 2022 (11:59pm UTC/GMT, firm) Notification: October 25, 2022 Final Manuscript Due: November 10, 2022 Submission site: http://www.swinflow.org/confs/2022/ispa/submission.htm Publication: Proceedings will be published by IEEE CS Press. Special issues: Distinguished papers will be selected for special issues in Journal of Parallel and Distributed Computing, Concurrency and Computation: Practice and Experience, Journal of Computer and System Science =========== Introduction The IEEE ISPA 2022 (20th IEEE International Symposium on Parallel and Distributed Processing with Applications) is a forum for presenting leading work on parallel and distributed computing and networking, including architecture, compilers, runtime systems, applications, reliability, security, parallel programming models and much more. During the symposium, scientists and engineers in both academia and industry are invited to present their work on concurrent and parallel systems (multicore, multithreaded, heterogeneous, clustered systems, distributed systems, grids, clouds, and large scale machines). The IEEE ISPA follows the tradition of previous successful IEEE ISPA conferences in the years from 2003 to 2021 in Asia, Europe, Australia and North America. It will feature sessions of regular presentations, workshops, tutorials and keynote speeches. IEEE ISPA is sponsored by the IEEE Technical Committee on Scalable Computing (TCSC) and the IEEE Computer Society. IEEE ISPA is particularly interested in research addressing heterogeneous computing with the use of accelerators, mobile computing, approximate computing, tools and methodologies to improve the quality of parallel programming and applying generic computing approaches to networks, in particular Software Defined networking and its applications. Scope and Topics (1) Systems and Architectures Track - Cloud computing and data center technology - Migration of computations - Multi-clouds environments, cloud federation, interoperability - Energy management and Green Computing - Wireless and mobile networks - Internet-Of-Things (IoT) - Social Networks, crowdsourcing, and P2P systems (2) Technologies and Tools Track - Building block processors: FPGA, multicore, GPU, NoC, SoC - Parallel and distributed algorithms - Tools/environments for parallel/distributed software development - Novel parallel programming paradigms - Programming models for cloud services and applications - Code generation and optimization - Compilers for parallel computers - Middleware and tools - Scheduling and resource management - Performance simulations, measurement, and evaluations - Reliability, fault tolerance, dependability, and security (3) Applications Track - High-performance scientific and engineering computing - Grid and cluster computing - Pervasive and ubiquitous computing - Databases, data mining, and data management - Big data and business analytics - Scientific cloud systems and services - Internet computing and web services - Application scenarios of IoT and ubiquitous computing - Experience with computational, workflow and data-intensive applications - Software Defined Networks and its applications Submission Guidelines Submissions must include an abstract, keywords, the e-mail address of the corresponding author and should not exceed 8 pages (or up to 10 pages with over length charge), including tables and figures in IEEE CS format. The template files for LATEX or WORD can be downloaded here. All paper submissions must represent original and unpublished work. Each submission will be peer reviewed by at least three program committee members. Submission of a paper should be regarded as an undertaking that, should the paper be accepted, at least one of the authors will register for the conference and present the work. Submit your paper(s) in PDF file at the submission site: https://edas.info/N30045 Publications Accepted and presented papers will be included into the IEEE Conference Proceedings published by IEEE CS Press. Authors of accepted papers, or at least one of them, are requested to register and present their work at the conference, otherwise their papers may be removed from the digital libraries of IEEE CS and EI after the conference. Distinguished papers will be selected for special issues in Journal of Parallel and Distributed Computing, Concurrency and Computation: Practice and Experience, Journal of Computer and System Science. Honorary Chairs Albert Zomaya, The University of Sydney, Australia Hai Jin, Huazhong University of Science and Technology, China General Chairs Willy Susilo, University of Wollongong, Australia Beniamino Di Martino, Universita' della Campania "Luigi Vanvitelli", Italy Laurence Yang, St. Francis Xavier University, Canada Program Chairs Haipeng Dai, Nanjing University, China Rajiv Ranjan, Newcastle University, UK Massimo Cafaro, University of Salento, Lecce, Italy Workshop Chairs Rodrigo Calheiros, Western Sydney University, Australia Wei Zheng, Xiamen University, China

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[CFP] ACM e-Energy 2023 Call for Papers
by Tan Rui (Assoc Prof) 11 Aug '22

11 Aug '22

░░░░░░ ACM e-Energy 2023 Call for Papers ░░░░░░ [Apology if you receive multiple copies of this message] Orlando, Florida during June 16 - 23, 2023 Detailed CFP can be found at https://energy.acm.org/conferences/eenergy/2023/cfp.php Submission site: https://eenergy23.hotcrp.com> ACM e-Energy is the premier forum for research at the intersection of computing and communication technologies with energy systems. It has established a strong track record for high-quality research in the application of computing and networked systems to make legacy systems more energy-efficient and in the design, analysis, and development of sustainable and innovative energy systems. The 14th ACM International Conference on Future Energy Systems (ACM e-Energy 2023) and its co-located tutorials and workshops will be held in Orlando, Florida during June 16 - 23, 2023. By bringing together researchers in a single-track conference designed to offer significant opportunities for personal interaction, it is a major forum for shaping the future of this area. ACM e-Energy 2023 will be held jointly with ACM Federated Computing Research Conference (FCRC) 2023 (https://fcrc.acm.org/), which assembles a spectrum of affiliated research conferences and workshops. ACM e-Energy 2023 attendees are free to attend the technical sessions of all FCRC conferences and workshops. We seek high-quality papers at the intersection of computing and communication technologies with smart and sustainable energy systems. We welcome submissions describing conceptual advances, as well as advances in system design, implementation, and experimentation, and we explicitly welcome inter- or trans-disciplinary work. ACM e-Energy is committed to a fair, timely, and thorough review process with sound and detailed feedback. IMPORTANT DATES Fall Deadline (1st deadline) September 16th, 2022, 23:59 AoE: Abstract registration deadline September 23th, 2022, 23:59 AoE: Paper submission deadline November 4th, 2022: Author notification (accept, reject, or revise-&-resubmit) November 25th, 2022, 23:59 AoE: Revision submission deadline December 9th, 2022: Revision notification December 19th, 2022, 23:59 AoE: Camera-ready submission Winter Deadline (2nd deadline) January 27th, 2023, 23:59 AoE: Abstract registration deadline February 3rd, 2023, 23:59 AoE: Paper submission deadline March 31st, 2023: Author notification (accept, reject, or revise-&-resubmit) April 21st, 2023, 23:59 AoE: Revision submission deadline May 12th, 2023: Revision notification May 26th, 2023, 23:59 AoE: Camera-ready submission Relevant topics for ACM e-Energy include, but are not limited to the following: AI/ML and data analytics, e.g., for tackling climate impact of energy systems Algorithmic approaches to energy system problems Applications of cyber-physical systems and Internet-of-Things (IoT) to smart energy systems Modelling and analysis of multimodal and cross-sectoral energy systems Automation and control of smart grids Demand-side management, including innovative pricing and incentive design Distributed ledger systems for energy systems Economics and business models for smart energy systems, including aggregators and prosumers Electricity market and electricity supply chain measurement, modeling, and analysis Electric vehicles and energy-efficient transportation systems Distributed energy resources, including energy storage and renewable resources Energy-efficient computing and communication, including data centers Measurement and accounting of greenhouse gas emissions arising from the energy sector Microgrid and distributed generation management and control Modeling and understanding user behavior in energy systems Sizing, monitoring, and control of energy systems for smart grids, smart buildings, and smart cities Privacy, cybersecurity, and resiliency of smart grid infrastructure Design, implementation, analysis, and experience with large-scale, operational systems in the field General Chair Kirk W. Cameron (Virginia Tech, USA) Program Chairs Xiaofan (Fred) Jiang (Columbia University, USA) Zheng Ma (University of Southern Denmark, Denmark) Rui Tan (Nanyang Technological University, Singapore) ________________________________ CONFIDENTIALITY: This email is intended solely for the person(s) named and may be confidential and/or privileged. If you are not the intended recipient, please delete it, notify us and do not copy, use, or disclose its contents. Towards a sustainable earth: Print only when necessary. Thank you.

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CFP: IEEE ISPA2022 (Parallel and Distributed Processing with Applications), Dec. 2022, Melbourne, Australia
by Jesson Butt 31 Jul '22

31 Jul '22

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(CFP Due Extended Sept 03) AHPC/CCCT2022- HPC/Cloud Computing, Oct 3-6, Hybrid, USA
by Pierre F. Tiako 15 Jul '22

15 Jul '22

[Due to numerous requests (Heatwave, Summer Break, Pandemic, Start of Semester), the paper submission deadline is extended to Sept 03] --- Call for Abstracts and Papers ------------- AHPC/CCCT2022- HPC/Cloud Computing Downtown Oklahoma City, OK, USA & Online October 3-6, 2022 OkIP Published & WoS/Scopus Indexed Submission Deadline Extended: Sept 03, 2022 Extended versions of the best papers will be considered for journal publication. *** Contribution Types (One-Column IEEE Format Style): - Full Paper: Accomplished research results (10 pages) - Short Paper: Work in progress/fresh developments (6 pages) - Extended Abstract/Poster/Journal First: Displayed/Oral presented (3 pages) >>> Cloud Computing and Technologies (CCCT) https://eventutor.com/e/CCCT002 * Areas: - Cloud Concepts - Applications and Services - Security/Privacy/Compliance - Platform/Provisioning/Storage - Infrastructure/AI in the Cloud * Technical Program Committee https://eventutor.com/event/20/page/58-committee >>> Advances in High-Performance Computing (AHPC) * Areas: - HPC Concepts - Parallel Computing - Mobile & Wireless Computing - Network Architecture/System - HPC AI/Simulation/Security - HPC Applications * Technical Program Committee https://eventutor.com/event/20/page/58-committee Please feel free to contact us for any inquiries at: info(a)okipublishing.com

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CFP-Due July 11: AHPC 2022 High-Performance Computing, Oct 3-6
by Pierre F. Tiako 20 Jun '22

20 Jun '22

[Apologies if you receive multiple copies] --- Call for Abstracts and Papers ------------- 2022 OkIP International Conference on Advances in High-Performance Computing (AHPC) Downtown Oklahoma City, OK, USA & Online October 3-6, 2022 https://eventutor.com/e/AHPC002 OkIP Published & SCOPUS/WoS Indexed Submission Deadline: July 11, 2022 Extended versions of the best papers will be considered for journal publication. * Contribution Types (One-Column IEEE Format Style): - Full Paper: Accomplished research results (10 pages) - Short Paper: Work in progress/fresh developments (6 pages) - Extended Abstract/Poster/Journal First: Displayed/Oral presented (3 pages) * Areas: >> HPC Concepts - High-Performance Computing (HPC) - Performance | Scalability | Virtualization - Visualization | Simulation | Modelisation - Prediction | Program Optimization | Workloads - Algorithms | Networks | Efficiency |Scheduling - Compiler Design and Technologies - HPC Components and Technologies - Data Management and Transport | Compilation - Load Balancing | Resource Management - Fault Tolerance | Reliability | Availability - Multicore Algorithms | Operating System - Software Design and Implementations - Asynchronous Methods | Interconnects - Interconnections | Distributed Computing - High Scalability Computing | Accelerators - High-Performance Modeling/Software/Tools - Routing Protocols | Quality of Service - Allocation Framework | Benchmarks |... >> Parallel Computing - Parallelism | Automatic Parallelization - Programming Languages/Libraries/Tools - Programing Models/Environments - Parallel Metaheuristics/Application-Behavior - Parallel/Distributed Algorithms/Architectures - Energy Factors/Technological Factors - Partitioning and Optimal Scheduling - Systems Performance/Potential/Efficiency - Distributed/Heterogeneous Systems - System Synergy and Optimization - Advances in Parallel Models/Systems/Tools >> Mobile & Wireless Computing - Communication Modeling/Designing/Analysis - Mobile Communication Prediction/Control - Mobile/Wireless Systems-of-Systems - Mobile/Wireless Software and Hardware - Heterogeneous Wireless Networks - Medical Alert Devices/Privacy Consideration - Wireless Sensor Network | IoT Platforms - Mobile Network Loss Issues - Ultra-Dense Networks | Mobile Performance - Sensor Networks and Embedded Applications - Innovative Mobile/Wireless Networks/Systems >> Network Architecture/System - Interconnection Networks and Architectures - Memory Architecture Evaluation - I/O, File Systems, and Memory Systems - Power-Efficiency | Systems-on-Chip - System Modeling Methodologies - Fault-Tolerant Algorithms/Systems - Scalable Servers and Systems - Shared Memory Implementation - Memory Exploitation/Management Techniques - Network Storage Systems and Services - Cluster Management/Architecture - Heterogeneous Architectures/Accelerators - Multi/Many Core/CPU Framework/Systems - Micro-Architecture Techniques - Reconfigurable Architectures/Systems >> HPC AI/Simulation/Security - Surrogate Modeling | Machine Learning - Neural Architecture Performance - Deep Learning | Agent-based Modeling - Intelligent Algorithm| Artificial Neural Network - Distributed Deep Learning Systems - Hybrid/Complex System Modeling/Simulation - Advanced/Stochastic Simulation Frameworks - Security, Policy, and Management Issues - Configuration, Policy, and Management issues - Graph Partitioning/Analysis/Analytics - Spiking Neural P Systems | Data Integrity - Enabling Blockchain-as-a-Service - Fuzzy Logic Approach | Streaming Algorithms - Association Rule Mining | Anomaly Detection - Big Data Analytics and Applications >> HPC Applications - HPC Applications and Case Studies - Grid/Cloud/Fog/Bio-inspired Computing - Scientific/Engineering/Commercial Workloads - Augmented/Virtual Reality - Biotechnology, Nanotechnology, and Finance - Space Research, Urban Planning, and Energy - Teleimmersion | Collaborative Applications - Media/Entertainment Industry | Visualization - Workflow Management | HPC Resiliency - Advanced Computational Methods/Applications >> Important Dates: - Submission Deadline: July 11, 2022 - Notification Due: August 01, 2022 - Camera-ready Due: August 22, 2022 >> Technical Program Committee https://eventutor.com/event/20/page/58-committee Please feel free to contact us for any inquiries at: info(a)okipublishing.com -------- Pierre Tiako General Chair

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A research grant is available within the RIPARTI regional projects
by Massimo Cafaro 17 Jun '22

17 Jun '22

A research grant is available at the University of Salento, Lecce, Italy. The research shall be developed in close collaboration with Echolight (https://www.echolightmedical.com, 12 months out of 18 in total), and is related to the following topic: The main goal of the project is to improve the tuning and calibration process of noninvasive diagnostic imaging devices used for imaging. One of the most critical steps during the implementation of a diagnostic imaging device is its calibration. In fact, poor calibration can lead to unreliable instrument performance with noisy images and the presence of unwanted artefacts that could mislead the diagnosis made by the physician. The calibration phase involves a repeated try-and-check procedure during which the instrument parameters are repeatedly changed in order to obtain images that are sharp and as closely matched as possible to the target reference. This phase often requires considerable time expenditure and expert supervision; moreover, if one considers that calibration is carried out both following the production of the diagnostic instrument but also after several months of its use in the operational context, it is easy to deduce that automating this process on the one hand would improve the diagnostic yield, and on the other hand would reduce downtime and recalibration. The project aims to improve and automate the calibration process by introducing machine learning techniques for image classification. The results of the project find application on all instruments used for imaging, whether they are based on MRI, computed tomography, X-ray or ultrasound techniques. In fact, the goal is to relate the configuration parameters of the instrument to the images it produces in order to eliminate noise and artefacts produced by misconfiguration. Despite this, in the project we will consider as a case study the images produced by an ultrasound-based device produced by Echolight S.p.A. Medical devices produced by Echolight S.p.A. exploit images derived from ultrasound scans (B-Mode) to automatically identify anatomical reference targets (lumbar vertebrae bone interfaces of the L1-L4 tract and proximal femur bone interface). Once the regions of interest (ROIs) are identified, a proprietary algorithm evaluates the spectral characteristics of selected portions of the raw ultrasound signal related to the analyzed bone tissues. From the analysis of the raw signal characteristics, a measure of the bone mineral density (BMD) of the analyzed anatomical sites is determined. In order to provide reliable, repeatable, and accurate BMD measurements, special calibration and testing procedures have been developed, however, which require several manual measurements and checks, resulting in a high human-time commitment and, consequently, introducing a risk of human error on the collection and interpretation of the collected measurements and results. Leveraging the image processing and image classification techniques developed within the project, the algorithm will provide output indicative of the presence of artefacts or other alterations in the performance of the ultrasound system in production in order to possibly intervene with further modifications and calibrations. As part of the project, standard conditions for conducting tests will also be defined through the use of specific ultrasound phantoms provided by the company. Prof. Italo Epicoco (italo.epicoco(a)unisalento.it) is the scientific responsible for this research grant. 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 are, attached, an unofficial English translation of the call and the corresponding application and self declaration forms, translated in English. NOTE: Foreign candidates are strongly encouraged to contact Prof. Epicoco by email if they need help/support in order to prepare their application: hewill be glad to assist. ******************************************************************************************* 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 https://www.massimocafaro.it 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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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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computational.science