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Post Doctoral Research Associate - Biomedical Engineering - Dutta Lab

Job Description


Apply now Job no:515408
Work type:Post Doc (Amherst Only)
Location:UMass Amherst
Department: Biomedical Engineering
Union: Post Doc
Categories:Postdoctoral Research Associate, Research, College of Engineering


About UMass Amherst

UMass Amherst, the Commonwealth's flagship campus, is a nationally ranked public research university offering a full range of undergraduate, graduate, and professional degrees. The University sits on nearly 1,450-acres in the scenic Pioneer Valley of Western Massachusetts and offers a rich cultural environment in a bucolic setting close to major urban centers. In addition, the University is part of the Five Colleges (including Amherst College, Hampshire College, Mount Holyoke College, and Smith College), which adds to the intellectual energy of the region.

Job Description

The Biomedical Imaging and Data Science Lab (BIDSLab: https://www.bidslab.org ) directed by Dr. Joyita Dutta in the Department of Biomedical Engineering at UMass Amherst seek a motivated postdoctoral fellow with a Ph.D. in electrical engineering, computer science, or a related field. The individual will work on research projects on signal processing and artificial intelligence (AI) for medical image enhancement and reconstruction, brain network analysis, and personal health monitoring.

Responsibilities include:

  • Working with multimodality medical datasets, including images, graphs, and time series to build interpretable AI models for human health and disease.
  • Working on interdisciplinary research ideas that involve collaborating with clinicians and scientists from academia and industry.
  • Collaborating with and providing guidance to BIDSLab graduate students.
  • Publishing peer-reviewed journal/conference papers and presenting original research at conferences.
  • Contributing to the overall success of BIDSLab and the BME department by performing other duties and responsibilities as assigned.

Requirements

  • A completed Ph.D. degree in electrical engineering, computer science, or a related field.
  • Proficiency in Python and MATLAB and one or more deep learning suites (e.g., TensorFlow, PyTorch, etc.).
  • A strong publication record as evidenced by peer-reviewed journal/conference papers.
  • Strong mathematical, coding, and communication skills.

Additional Information

The Department of Biomedical Engineering represents the core of biomedical engineering research and teaching at UMass Amherst and leverages synergies with other departments in the College as well as with the new Institute of Applied Life Sciences (IALS: https://www.umass.edu/ials/ ), which was established in 2014 with an investment of more than $150 million from the Massachusetts Life Science Center and the University. IALS manages more than 30 core equipment facilities to enable its translational mission (https://www.umass.edu/ials/core-facilities ).

Application Instructions

Applicant should include a cover letter, their CV, copies of two peer-reviewed published papers, and names and professional email addresses of three references. Please apply online by December 31, 2022, for full consideration, although the position will remain open until filled. 

UMass Amherst is committed to a policy of equal opportunity without regard to race, color, religion, gender, gender identity or expression, age, sexual orientation, national origin, ancestry, disability, military status, or genetic information in employment, admission to and participation in academic programs, activities, and services, and the selection of vendors who provide services or products to the University.  To fulfill that policy, UMass Amherst is further committed to a program of affirmative action to eliminate or mitigate artificial barriers and to increase opportunities for the recruitment and advancement of qualified minorities, women, persons with disabilities, and covered veterans.  It is the policy of UMass Amherst to comply with the applicable federal and state statutes, rules, and regulations concerning equal opportunity and affirmative action.


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