we are looking for bright and highly motivated student for one PhD
position at the Department of Information Engineering at the University
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
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 [log in to unmask] 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: [log in to unmask]
Carlo Vallati, PhD
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
E-mail:[log in to unmask]
[log in to unmask]
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