Research Scientist, Health 0.0

by Kelley Shepard

Nov. 21, 2018

Job Description

The MIT Media Lab requires a research scientist to develop reinforcement learning and deep neural network (DNN)-emergent architectures for biomedical and clinical trial datasets for improving human health. Position will work closely with Dr. Pratik Shah, students, and research staff within the Health 0.0 research program at The MIT Media Lab that creates novel intersections between engineering, medical imaging, machine learning, and medicine to improve health outcomes for patients.


  • Devise novel machine learning methods that learn from pre-clinical data (biological, omics and animal models), clinical trials with drugs and vaccines, and electronic medical data from patients.
  • Use existing machine learning techniques and models (AlexNet, ImageNet, MNIST etc) for processing multimodal datasets.
  • Contribute to research publication.
  • Contribute to project and financial management and managing interactions with collaborators and funding agencies including, reporting, developing and implementing project plans, monitoring and evaluating processes, tools, and team performance, and ensuring accurate data reporting and timely deliverables.
  • Participate in diverse activities as required, including research group meetings and
    supporting to other group members, PhD students and undergraduate researchers.

This appointment is for one year with the possibility of extension based on funding availability and research priorities.



  • Ph.D. degree in computer science;
  • Experience in machine learning methodologies such as regression/classification, unsupervised/supervised/semi-supervised learning, ensemble methods, reinforcement learning, and deep learning;
  • Professional experience writing software in Python, Java, or C++, preferably within a team environment (version control, issue tracking, code review);
  • Strong knowledge of Linux and high-performance computing environments;
  • Knowledge of predictive analytics/statistical and mathematical modeling/data mining algorithms;
  • Excellent data analysis, scientific writing and presentation skills;
  • Track record of research publications;
  • Ability to work effectively and productively in a diverse, team-based environment.


  • Interest in solving grand challenges in health and overall project coordination are particularly encouraged to apply;
  • Knowledge of biological sciences and clinical datasets is a plus but not essential;
  • Experience using reinforcement learning and DNNs is a plus and willingness to learn
    advanced deep learning approaches is expected.
  • To apply, go to and search for job ID# 16816 (direct link).

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