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Internship Machine Learning Postdoc Jobs in Massachusetts

MIT Human Resources

Postdoctoral Associate

Cambridge, MA · On-site +1

POSTDOCTORAL ASSOCIATE, Mechanical Engineering, will work under the direction of Prof. Sherrie Wang ... Will develop and implement machine learning models for local weather forecasting and uncertainty ...

Worcester Polytech

Post-Doctoral Fellow

Worcester, MA

$45K - $70K/yr

The position also involves the integration of machine learning and AI-assisted approaches into materials modeling workflows. The postdoctoral researcher is expected to work independently, develop ...

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Postdoctoral Fellow in Biomedical Informatics (Cai Lab)

Postdoctoral Fellow in Biomedical Informatics (Cai Lab)

Harvard University

Cambridge, MA • On-site

$54K - $73K/yr

Full-time

Re-posted 23 days ago

Harvard University rating

8.4

Company rating: 8.4 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

80th of 553 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral Fellow in Biomedical Informatics (Cai Lab)
School
Harvard Medical School
Department/Area
Biomedical Informatics
Position Description
A Postdoctoral Research Fellow position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic research group focused on several synergistic goals: generating actionable Real-World Evidence (RWE) from multi-institutional Electronic Health Records (EHR), improving the generalizability of clinical evidence across diverse populations using multi-source and multi-modal data, and accelerating drug discovery by leveraging these rich, integrated datasets. This role offers a unique opportunity to develop methodological innovations that bridge the gap between computational theory and impactful clinical application.
We are seeking a highly motivated individual with a strong statistical and machine learning background. The ideal candidate will have existing expertise in several of the following areas, aligned with our research focus: 1) Causal inference, invariant learning and representation learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing.
Basic Qualifications
Candidates must hold a Ph.D. in a quantitative field, such as statistics, biostatistics, computer science, or a related discipline. Success in this position requires strong quantitative research capabilities and demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must possess excellent written and oral communication abilities to effectively disseminate research findings and collaborate within a multidisciplinary team.
Additional Qualifications
Special Instructions
Contact Information
Mo Moro
Contact Email
mohammed_moro@hms.harvard.edu
Salary Range
Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines
Minimum Number of References Required
Maximum Number of References Allowed
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