PhD positions in QMUL in Computer Vision and Machine Learning

郭炜炜上交

PhD positions in Computer Vision and Machine learning are available for
candidates with CSC scholarships in the school of Electronic Engineering
and Computer Science in Queen Mary University of London. Candidates that
are interested in the following two topics, or in closely related areas
in Computer Vision and Affective Computing, are encouraged to contact
Ioannis Patras (i.patras@qmul.ac.uk) directly via email with a copy of
their CV.


Project 1) Deep Neural Networks for Affective Computing.

This project aims at developing Machine Learning methodologies for
analysing Human Signals for analysis and recognition of affective and
cognitive states. The emphasis will be on Deep Learning methodologies
for analysis of neurophysiological signals such as EEG and physiological
signals such EDA and ECG and applications in interaction with multimedia
content, building on the works of Koelstra and Patras
(http://www.eecs.qmul.ac.uk/~ioannisp and
http://www.sanderkoelstra.nl/research).


Project 2) Deep Neural Networks for Human Behaviour Analysis

This project aims at Machine Learning methodologies for analysis at
various levels of human behaviour, e.g. recognition of human actions,
affective states and the interactions between humans and their
environment. At a lower level this involves modeling and recognition of
objects, facial expressions, body poses/gestures and actions. A
particular focus will be on Deep Learning architectures and on
especially on ways of transferring knowledge between the different tasks.


The researcher will be based in the School of Electronic Engineering and
Computer Science of Queen Mary, University of London.

For further information, please email directly i.patras@qmul.ac.uk,
attaching a copy of your CV.


Ioannis Patras is a Reader (Associate Professor)  in Queen Mary,
University of London. His research is in an area that can be described
as 'Looking at / sensing people'. It focuses on the analysis of image
sequences that depict human actions, including facial expressions and
body gestures. This ranges from low level analysis of facial and body
motion (e.g. tracking of facial features, head and body pose estimation)
to higher level recognition of action categories, facial expressions and
affective states. The applications that he is interested in are in are
in the fields of Affective Computing and Human Centred Media. A recent
line of work is on the crossroads of these two fields and analyses human
behaviour while interacting with multimedia content. This includes
analysis of facial expressions, gaze, and EEG signals in order to learn
to annotate with a human in the loop. His research uses methodologies
and contributes to the fields of Computer Vision, Pattern Recognition
and Machine Learning. He is a Senior Member of IEEE.

Queen Mary University of London (QMUL) is a Russell group research
intensive university and one of the largest colleges of the University
of London. QMUL is a research-focused higher education institution with
over 80% of research staff working in departments where research is of
international or national excellence (according to the latest UK's
Research Assessment Exercise), compared to a national benchmark of 55%.
The School of Electronic Engineering and Computer Science has highly
reputed research expertise in multi-sensor data processing, distributed
signal processing, scene analysis, vision and audio analysis,
distributed systems, performance and social networks data analysis and
research, human-human and human-computer interaction, risk assessment
and decision analysis.