Applications are invited for multiple postdoctoral fellowship positions working 
with Andrew McCallum at University of Massachusetts Amherst in various 
combinations of
natural language processing;
deep learning and other areas of machine learning;
knowledge representation and logical reasoning, with deep learning;
knowledge bases;
application areas are flexible; current work includes reasoning about the 
scientific literature, career paths, peer review.

I will be available to meet in person with interested candidates at the 2016 
NAACL, ICML and UAI conferences.

The postdocs will be part of a large research group with many collaborative 
opportunities, both within the group as well as across the UMass Amherst's 
Center for Data Science and College of Information and Computer Sciences (which 
includes other faculty working in deep learning, NLP, machine learning, 
computer vision, databases, and information retrieval).  UMass Amherst ranks 
4th in AI among US universities by publications in top-tier venues.  The UMass 
Amherst Center for Data Science recently announced at $15m gift from MassMutual 
to further expand faculty hiring.   Surrounded by five colleges, Amherst is 
located in bucolic western New England within day-trip range of both Boston and 
New York.

Successful applicants will have extensive research experience, an excellent 
publication record in one or more of the research areas above, creativity, and 
strong communication, experimentation, and coding skills.

Examples of McCallum's previous postdocs and PhD students with academic 
placements:
Sebastian Riedel, University College London
Charles Sutton, University of Edinburgh
David Mimno, Cornell
Sameer Singh, UC Irvine
Jinho Choi, Emory
Benjamin Roth, Munich University
Chris Pal, École Polytechnique de Montréal

If you are interested, or have questions, or would like to meet at NAACL, ICML 
or UAI, please send email to Andrew McCallum <[email protected]> and Pam 
Mandler <[email protected]>.

The University of Massachusetts is an Affirmative Action/Equal Opportunity 
employer.

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