21 machine-learning "MIT" research jobs

Senior User Experience Researcher

EXPERIENCE RESEARCHER, Open Learning, to work with the product team on identifying, defining, and refining research questions. Will conduct in-depth user research using multiple methods such as interviews

Postdoctoral Associate

innovative research dedicated to the integration of high-fidelity 3D magnetohydrodynamics (MHD) simulations in machine learning models trained with experimental data; interface with broader teams studying

Post-Doctoral Research Associate

, machine learning-assisted mapping and reconstruction of brain-wide circuitry, behavioral clustering, cell-type and action-specific Cal-light tagging, closed-loop optogenetic manipulation, calcium imaging

Pre-Doctoral Fellowship (Prof Gabaix)

this academic year. We’re looking for two types of predoctoral fellows: “Machine learning” track: you’re very skilled in machine learning, in particular large language models. Ideally, you would have a Masters in

PhD/Postdoctoral Researcher (m/f/d) Underwater Robot Manipulation in Environmental Monitoring Scenarios

mentality for cutting-edge research in various fields including robotics, machine learning and systems intelligence. An exceptional opportunity to experience research in a highly inspiring international

Postdoctoral Researcher positions in Statistical/Generative AI applications in genetics

Health on Develop machine learning models for multi-ancestry functional fine-mapping and polygenic risk score models. Build graph-embedding models on biomedical and cancer knowledge graphs. Performing GWAS

Research Assistant in the Division of Science (Computer Science)

of this project is to add support for automatic code optimization in Tiramisu.  In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module

Research assistants - National Centre for Register-based Research, Aarhus University

. using programs like PLINK, bigsnpr, regenie, BOLT-LMM, GCTA, LDSC, LDAK, LDpred1/2, PRS-CS, SBayesR, PRSice. Machine learning approaches, e.g. deep learning, autoencoders, XGboost, or penalized regression

Postdoctoral Research Associate

collaborators on these projects, including Pawan Sinha at MIT, Alireza Ramezani at Northeastern, Joo-Hyun Song at Brown University, and David Lin at Massachusetts General Hospital. The research is supported by

Postdoctoral Associate - Earth and Atmospheric Sciences

at Cornell University, but the project will include close collaboration with colleagues at Penn State and MIT, as well as a machine learning-focused geothermal start-up (Strabo Analytics). The entire project