Senior Research Support Associate, Machine Learning and Medical Imaging, Health 0.0

Job Description

The MIT Media Lab requires a senior research support associate to develop artificial intelligence and machine learning algorithms from biomedical imaging datasets.  Understanding mechanisms used by emergent deep neural network (DNNs) architectures to learn and classify medical information from multispectral images will be the key focus. Position will work closely with Dr. Pratik Shah, students, and research staff within the Health 0.0 research program at the Media Lab that creates novel intersections between engineering, medical imaging, machine learning, and medicine to improve health outcomes for patients.  This appointment is for the term of one year with possibility of extension based on funding and research priorities.


  • Conceptualize and build tools for visualizing and classifying high dimensional imaging data;
  • Develop software tools with applications in data annotation, such as labels on images, visualization and explanation of machine learning models, and health diagnostics; leverage existing libraries (e.g., OpenCV, Theano, Caffe, and TensorFlow);
  • Support other group members, PhD students, and undergraduate researchers.


  • BS or MS in computer science or related field;
  • Minimum of one year of experience in developing software using Python, C++, Java;
  • Experience using Linux and version control;
  • Familiarity with data processing techniques for text/images/videos/time series;
  • Familiarity with developing computer vision and machine learning algorithms for imaging datasets using Python (SciPy, NumPy Scikit-learn);
  • Web development experience including frontend (HTML5, JavaScript, CSS3, Bootstrap, JQuery/AngularJS/React/similar framework), backend (Python/NodeJS, Django/Express/Sails or other MVC framework), and database (MySql/MongoDb/other traditional database);
  • Ability to work effectively and productively in a diverse, team-based environment;
  • Candidates with a data sciences or engineering background and interest in solving grand challenges in health are particularly encouraged to apply.

To apply, go to and search for job ID # 16775 (direct link).

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