[visionlist] [Jobs] PhD scholarship at University of Liverpool

[Anh Nguyen]

*PhD Scholarship: Development of Energy Efficient Machine Learning Models*

*Overview*
This project is a part of a 4-year dual PhD programme between National
Tsing Hua University (NTHU) in Taiwan and the University of Liverpool. As
part of the NTHU-UoL Dual PhD Award, students are in the unique position of
being able to gain 2 PhD awards at the end of their degree from two
internationally recognised world leading Universities, as well as
benefiting from a rich cultural experience. Students can draw on large
scale national facilities of both countries and create a worldwide network
of contacts across 2 continents. It is planned that students will spend 2
years at NTHU, followed by 2 years at the University of Liverpool. Both the
University of Liverpool and NTHU have agreed to waive the tuition fees
(worth £25,000 per year for international students at University of
Liverpool) for the duration of the project and stipend of TWD11,000/month
will be provided as a contribution to living costs (the equivalent of £280
per month when in Liverpool). Additional funding could be available for
excellent candidates.

*Project Description*
Energy consumption has been a crucial concern due to the changes in global
climate conditions during the past decades, and efficient usage of energy
has attracted the attention of many researchers. However, since the
prevalent usage of deep neural networks (DNNs), the need of energy
consumption for training DNNs, especially for large-scale models (e.g.,
ResNet, Transformer, GPT-3, etc.), have surged, posing significant
challenges to global energy and hence the climate conditions. These DNN
models have been widely employed in various application domains such as
computer vision (CV), self-driving cars, autonomous drones, surveillance
cameras, natural language processing (NLP), and so on. Executing these DNNs
typically requires the usage of many graphic processing units (GPUs) or DNN
accelerators. As the sizes of those DNNs increase, the computational cost
and the required inference power also grow.

Preliminaries studies show that light-weight models can be achieved during
the testing phase [1][2], however, the training phase of big DNN remains
computationally expensive. As a result, in this proposal, we plan to
develop efficient DNN models, especially for vision and robotic
applications (which usually require large-size DNN models), such that their
inference and the training costs can be reduced. Specifically, the target
research and application domains considered in this proposal include DNN
models for CV, federated learning, and / or control models based on
reinforcement learning (RL).

[1] Tran, Minh Q., Tuong Do, Huy Tran, Erman Tjiputra, Quang D. Tran, and
Anh Nguyen. “Light-weight deformable registration using adversarial
learning with distilling knowledge.” IEEE Transactions on Medical Imaging
(2022).
[2] Chang, Chin-Jui, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei
Hong, and Chun-Yi Lee. “Reducing the Deployment-Time Inference Control
Costs of Deep Reinforcement Learning Agents via an Asymmetric
Architecture.” In IEEE International Conference on Robotics and Automation
(ICRA), 2021.

*Deadline:* As soon as possible. The candidates will be evaluated on a
rolling basis.

*How to Apply:*
https://www.csc.liv.ac.uk/~anguyen/PhD-scholarship-Development-of-Energy-Efficient-Machine-Learning-Models/
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