Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 13-Jul-26 Location: Cambridge, Massachusetts Categories: Academic/Faculty Biological/Biomedical Sciences Internal
-
awards in more than 120 countries for U.S. citizens to teach, conduct research, and carry out professional projects around the world. Location, activity type, and eligibility vary across awards
-
deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific
-
modern machine learning and a strong record of research accomplishment who are excited to build brain foundation models and other AI systems that advance our understanding of neural activity, brain
-
Description Join our dynamic research team at Harvard University and spearhead groundbreaking research at the intersection of generative AI, multimodal learning, and Earth sciences. We are seeking a highly
-
will lead and participate in observations and data analysis across the electromagnetic spectrum, or will lead work on machine learning classification of optical transients. Applicants with previous
-
, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in
-
data in near-term era quantum computers. Applicants with backgrounds in quantum information or particle physics are both encouraged to apply. Candidates with strong expertise in machine learning, quantum
-
diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership
-
strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal