Research - Laboratory/Non-Laboratory, Staff/Administrative
Working at MIT offers opportunities, an environment, a culture - and benefits - that just aren't found together anywhere else. If you're curious, motivated, want to be part of a unique community, and help shape the future - then take a look at this opportunity.
SENIOR RESEARCH SUPPORT ASSOCIATE , Media Lab , to work at the intersection of machine learning, bioinformatics, and computer vision. Will work closely with Dr. Pratik Shah, students and research staff within the Health 0.0 research program at the Media Lab which brings together life sciences, biotechnology, foundations, universities, patients, and health-oriented Media Lab member organizations. Responsibilities include training novel machine learning models for classification, prediction and analysis of bioinformatics, microbiology, and health diagnostic datasets; and supporting other group members, Ph.D. students, and undergraduate researchers.
REQUIRED : training in the use of bioinformatics or computational biology tools and datasets; experience with machine learning software development and engineering practices, including TensorFlow, Caffe, and Torch; GPU programming experience, including OpenCL and CUDA; experience with software development practices, including git-based version control and continuous integration; expertise in sparse matrix algebra and machine learning; training in statistics and experience in the application of statistical methodologies, including generalized linear models to biological research; demonstrated expertise programming (e.g., Perl/Python, BioPerl, Java, R, MySQL); expertise in UNIX-based computational environments; and the ability to work effectively and productively in a diverse, team-based environment. B.S. in computer science preferred. Candidates with strong computer science and data sciences backgrounds with an interest in solving grand challenges in health and medicine are encouraged to apply. Job #16776-4
This is a one year appointment with the possibility of extension based on funding and research priorities.
Internal Number: 16776
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