Sort by
Refine Your Search
-
Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
-
, machine learning and AI, statistical computing, big data and AI applications and prediction in biology, medicine and infectious diseases. Potential research projects include (but are not limited
-
We highly recommend reviewing our department website’s Faculty pages to learn more about our faculty, their labs, and their research interest before applying. Please apply through the ARIeS portal
-
native peoples, or peoples of African, Asian, or Hispanic descent. The fellowship includes the requirement to teach one course per year (ideally in the fall term), to participate in a fellowship program
-
to prioritize work and coordinate research protocols with lab members Excellent attention to detail and organization skills Interest in learning and strengthening existing skillsets Special Instructions We highly
-
applications for a Postdoctoral Fellow with Professor Pragya Sur. Professor Sur’s lab focuses on research in high-dimensional statistics, machine learning theory, or more broadly, mathematical foundations of AI
-
dramatic upheaval as a result of rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and
-
to have a strong background in the foundations of machine learning. Special Instructions Required application documents include a cover letter, CV, a statement of research interests, and up to three
-
at the Institute and affiliated academic departments. What you’ll do: Designing, developing, and deploying modern AI/ML models—including deep learning, foundation models, multimodal architectures, and generative
-
part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel quantitative methods at the interface of statistical learning, experimental design