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@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@unipi.it