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Postdoctoral Fellowship-Machine Learning & Health Analytics in the
Department of Computer Science and Engineering at the University of
Connecticut
About University of Connecticut:
The University of Connecticut (UConn) was founded in 1881, and is ranked
within the Top 20 Public Universities and within Top 60 National
Universities in the United States and is amongst the elite institutions
designated as “highest research activity” by the Carnegie Classification
of Institutions of Higher Education. UConn is fully accredited by the New
England Association of Schools and Colleges (NEASC) and is a research-
intensive university, a prestigious designation shared by only the nation's
top higher education institutions, with more than 70 focused research
centers where faculty, graduate students and undergraduates explore
everything from improving human health to enhancing public education and
protecting the country’s natural resources. Over the past two decades,
UConn and the State of Connecticut have invested heavily in building STEM
programs to meet next generation workforce needs. Over $2.8B has supported
capital projects to advance UConn’s teaching and research. In 2013, the
State committed an additional $1.7B through Next Generation Connecticut to
further support infrastructure development and the hiring of 200 new faculty
members. In addition, UConn Technology Park and the new $172M Innovation
Partnership Building offer resources that support basic research and
technology translation. The primary 4,400-acre (17.8 km2) campus at Storrs
is approximately a half hour's drive from Hartford and in between Boston and
New York City with an hour and half drive from each.
Job Description:
The Laboratory of Machine Learning & Health Informatics at UConn has
multiple Postdoctoral Fellow positions in the research area of Machine
Learning and its applications in Bioinformatics, Health Informatics, Drug
Discovery, and Medical Image Analysis. These positions will be funded by
multiple federal funding agencies, including NSF, NIH and Department of
Veteran Affairs. These projects will involve collaboration with research
teams distributed across several US universities, e.g., Yale University and
University of Pennsylvania, and international hospitals. The
responsibilities of these positions include:
* Development, implementation, evaluation and reporting of machine learning
models, algorithms, and software packages.
* Contributing to research manuscript preparation and scientific
presentations
* Assisting in the preparation of annual reports describing the progress of
the research to funding agencies.
* Supporting the collaboration with other institutions of the research team.
* Presenting at site visits and other project team meetings including
teleconference and video conference-based meetings.
* Mentoring junior Ph.D. students for specific projects and helping organize
project meetings.
Requirements:
* Ph.D. in Computer Science or Mathematics, or Statistics by the time of
appointment.
* Strong background in machine learning, statistical inference, mathematical
programming, and optimization techniques, as demonstrated by publications
and other professional activities, appropriate to the number of years in the
field.
* Expertise with programming languages appropriate to the specific field of
study and building/maintaining software systems.
* Expertise with parallel and/or distributed computing will be a plus.
* Experience with real-time streaming data analytics and parallel computing
will be a plus.
Additional Information:
* The annual salary is competitive and dependent upon experience.
* The appointment is expected to begin on August 28, 2017 (negotiable) for
two years and can be renewable on a yearly basis and contingent upon funding
availability.
Application Instructions:
To apply, please send a cover letter, resume, sample publications, and names
and contact information of three professional references to Prof. Jinbo Bi
at [email protected] or Prof. Song Han at [email protected] Review of
applications will begin on August 1st, 2017 and will continue until a
suitable candidate pool has been identified.
Department of Computer Science and Engineering at the University of
Connecticut
About University of Connecticut:
The University of Connecticut (UConn) was founded in 1881, and is ranked
within the Top 20 Public Universities and within Top 60 National
Universities in the United States and is amongst the elite institutions
designated as “highest research activity” by the Carnegie Classification
of Institutions of Higher Education. UConn is fully accredited by the New
England Association of Schools and Colleges (NEASC) and is a research-
intensive university, a prestigious designation shared by only the nation's
top higher education institutions, with more than 70 focused research
centers where faculty, graduate students and undergraduates explore
everything from improving human health to enhancing public education and
protecting the country’s natural resources. Over the past two decades,
UConn and the State of Connecticut have invested heavily in building STEM
programs to meet next generation workforce needs. Over $2.8B has supported
capital projects to advance UConn’s teaching and research. In 2013, the
State committed an additional $1.7B through Next Generation Connecticut to
further support infrastructure development and the hiring of 200 new faculty
members. In addition, UConn Technology Park and the new $172M Innovation
Partnership Building offer resources that support basic research and
technology translation. The primary 4,400-acre (17.8 km2) campus at Storrs
is approximately a half hour's drive from Hartford and in between Boston and
New York City with an hour and half drive from each.
Job Description:
The Laboratory of Machine Learning & Health Informatics at UConn has
multiple Postdoctoral Fellow positions in the research area of Machine
Learning and its applications in Bioinformatics, Health Informatics, Drug
Discovery, and Medical Image Analysis. These positions will be funded by
multiple federal funding agencies, including NSF, NIH and Department of
Veteran Affairs. These projects will involve collaboration with research
teams distributed across several US universities, e.g., Yale University and
University of Pennsylvania, and international hospitals. The
responsibilities of these positions include:
* Development, implementation, evaluation and reporting of machine learning
models, algorithms, and software packages.
* Contributing to research manuscript preparation and scientific
presentations
* Assisting in the preparation of annual reports describing the progress of
the research to funding agencies.
* Supporting the collaboration with other institutions of the research team.
* Presenting at site visits and other project team meetings including
teleconference and video conference-based meetings.
* Mentoring junior Ph.D. students for specific projects and helping organize
project meetings.
Requirements:
* Ph.D. in Computer Science or Mathematics, or Statistics by the time of
appointment.
* Strong background in machine learning, statistical inference, mathematical
programming, and optimization techniques, as demonstrated by publications
and other professional activities, appropriate to the number of years in the
field.
* Expertise with programming languages appropriate to the specific field of
study and building/maintaining software systems.
* Expertise with parallel and/or distributed computing will be a plus.
* Experience with real-time streaming data analytics and parallel computing
will be a plus.
Additional Information:
* The annual salary is competitive and dependent upon experience.
* The appointment is expected to begin on August 28, 2017 (negotiable) for
two years and can be renewable on a yearly basis and contingent upon funding
availability.
Application Instructions:
To apply, please send a cover letter, resume, sample publications, and names
and contact information of three professional references to Prof. Jinbo Bi
at [email protected] or Prof. Song Han at [email protected] Review of
applications will begin on August 1st, 2017 and will continue until a
suitable candidate pool has been identified.
