Data Scientist/Technical Associate 1
We are looking for a Data Scientist to implement, design and build supervised and unsupervised algorithms to predict human performance in behavioral, cognitive and physical tasks from their molecular features. This role is part of the data science and engineering team within the Fraenkel Lab, Dept. of Biological Engineering at Massachusetts Institute of Technology.
Technological advances in the acquisition of biological data over the past decade have resulted in an explosion of information. Computational integration of different data modalities remains a key challenge however, for researchers aiming to understand the underlying spatial and temporal biological processes. This role will address the said challenge through the application of state-of-the-art machine learning tools with the goal of generating new testable hypotheses.
As part of the data science and engineering team in the Fraenkel Lab this position would entail the following activities:
- Research, design, implement and compare novel ML solutions within the multi-omics integration domain for predicting biological aptitude and/or other clinical outcomes.
- Improve implementation of existing algorithms in multiple environments
- Identify and analyze biological databases to develop novel feature solutions and develop automated processing tools
- Collaborate with junior and senior computational staff on specialized projects within the natural language processing and imaging analyses domains
- Collaborate on analysis of existing model datasets and development of new sets based on existing architecture
- Assist in the development of research publications and/or related novel intellectual property related to this work, among other tasks.
- BS/BASc in Computer Science/Data Science/Analytics and 2 years of specialized related experience (which may include experience gained as an undergraduate).
- Experience with traditional and advanced machine learning techniques such as support vector machines, random forests and deep learning algorithms
- Experience with deep learning implementation platforms such as tensorflow and pytorch
- Experience working with large datasets, munging, feature engineering and developing machine learning models preferably on clinical datasets
- Background in biology would be helpful but not required
- Languages: Python/R/SQL
- Ability to work independently and as part of a team
- Ability to respond to the dynamic needs of the lab; must be self-motivated and detail-oriented
- Must have excellent organizational and communication skills, and ability to carefully document and maintain work in detailed written records.
Note: This position is not eligible for visa sponsorship. Please apply here (Req ID 19572): https://careers.peopleclick.com/careerscp/client_mit/external/jobDetails/jobDetail.html?jobPostId=20444&localeCode=en-us and reach out with questions to Dr. Swapnil Chhabra (email@example.com).
Postdoctoral, predoctoral, UROP and technical positions available.
We are looking for highly motivated candidates to participate in experimental and computational projects studying several human diseases using Systems Biology techniques. We obtain diverse data from ChIP-Seq, RNA-Seq, ATAC-Seq, WGS, reverse genetics and phospho-proteomics. We analyze these data to identify altered regulatory and signaling networks. Highly motivated applicants trained in either experimental or computational methods are welcome to apply.
Interested candidates should write to apply.fraenkel “AT” mit “DOT” edu
A note to prospective graduate students who have not yet been admitted to MIT:
At MIT students are admitted by graduate programs, not by individual faculty members. If you would like to learn more about the various graduate programs at MIT, including Biological Engineering and CSB, please feel free to contact me. However, it will not have any impact on your chances of admission to the programs.