Connectionists: Postdoctoral position in Hardware Acceleration for Deep Learning, University of Guelph

[Graham Taylor]

 The Machine Learning Research Group at the university of Guelph has a
fully-funded 1 year postdoctoral position in the area of FPGA-based
Hardware Acceleration for Deep Learning. The position will start on May 1,
2015. The postdoc will work closely with Dr. Graham Taylor and Dr. Shawki
Areibi and their respective research teams. The project involves developing
new representation learning and deep learning architectures and algorithms
with a focus on reducing the amount of training time to discover optimal
models.


The position is sponsored by a major international industry partner and
will require occasional travel to Toronto (e.g. one day every two weeks).
Company scientists and engineers are actively engaged in the project. The
team aims to publish the results of the research, with the candidate taking
a lead role in publication.


Ideal candidates will have a PhD in Computer Engineering, Computer Science,
Physics, Mathematics, or in a related area, and have a strong interest and
experience in hardware acceleration (specifically FPGA-based computing),
machine learning, statistics and/or scientific computing evidenced by a
strong publication record in top-tier conferences and journals. We will
consider candidates whose primary research area is Machine Learning or
Hardware Acceleration. Preference will be given to candidates who have
exposure to both areas. Experience with deep learning frameworks, e.g.
Caffe, Torch, Theano and FPGA software tools, e.g. OpenCL, Vivado HLS is an
asset.


Teaching is not a requirement of the position, but the candidate is
expected to supervise and mentor graduate and undergraduate students in a
research capacity.


Guelph is a vibrant university community 28 kilometres east of Waterloo and
100 kilometres west of downtown Toronto, with commuter train access to
both. It is consistently rated as one of Canada’s best places to live.


The Machine Learning Research Group at the University of Guelph consists of
approximately 15 students, postdocs, researchers and visitors. The group
focuses on deep learning and biologically-inspired computer vision. The
group holds two compute clusters (250 TFLOPS peak performance), which are
among the best GPU-based computing resources in Canada: a 10-node, 30 GPU
cluster of Nvidia Titan Black cards, and an 8-node, 64 GPU cluster of
Nvidia K80’s connected by Infiniband. The group is currently engaged in
several projects at the intersection of deep learning and high performance
computing.


The position is open to Canadian and foreign candidates. The salary is
$60,000 CAD per annum plus benefits. To apply, please send the following
documents in a single pdf-file to Dr. Taylor:


(1) Your Curriculum Vitae

(2) A brief statement of relevant research interests and/or experience
(preferably one page, at most two)

(3) Transcripts for previous degrees if available

(4) Names of two or three referees who are able to comment on the
applicant's qualifications. We will contact references of short-listed
candidates only.


We will start reviewing applications on February 15 and will continue until
a successful candidate is found. Only short-listed candidates will be
contacted.


-- 
Graham Taylor
Assistant Professor
School of Engineering, University of Guelph
http://www.uoguelph.ca/~gwtaylor
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