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.
RESEARCH SCIENTIST, HEALTH 0.0 , Media Lab , to develop reinforcement learning and deep neural network (DNN)-emergent architectures for biomedical and clinical trial datasets for improving human health. Will work closely with Dr. Pratik Shah, students, and researchers within the Health 0.0 research program which brings together life sciences, biotechnology, foundations, universities, patients, and health-oriented Media Lab member organizations. Responsibilities include devising 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; using existing machine learning techniques and models (AlexNet, ImageNet, MNIST, etc.) for processing multimodal datasets; contributing to research publications; contributing to project and financial management and managing interactions with collaborators and funding agencies (e.g., developing/implementing project plans, monitoring/evaluating processes/tools/team performance, ensuring accurate data reporting and timely deliverables); and participation in other activities.
REQUIRED : Ph.D. in computer science; experience with machine learning methodologies, e.g., regression/classification, unsupervised/supervised/semi-supervised learning, ensemble methods, reinforcement learning, and deep learning; professional experience writing software in Python, Java, or C++, preferably in a team-oriented environment (version control, issue tracking, code review); solid 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 publishing in research publications; and ability to work effectively and productively in a diverse, team-based environment. Those interested in solving grand challenges in health and overall project coordination are encouraged to apply. Knowledge of biological sciences and clinical datasets a plus, as is experience using reinforcement learning and DNNs. Willingness to learn advanced deep learning approaches is expected. Job #16816
This appointment is for one year with the possibility of extension based on funding availability and research priorities.
Internal Number: 16816
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