12 machine-learning "MIT" Postdoctoral positions

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

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

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

Postdoctoral scientists in psychiatric epidemiology and/or statistical genetics - National Centre for Register-based Research, Aarhus University

analyses using Danish register data and/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning

Post Doctoral Scholar

interested in candidates who can leverage machine, deep learning, and statistical methods to monitor species distributions and integrate biodiversity records from multimodal data sources to understand

Postdoctoral Research Associate

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 the National

Postdoctoral researcher, Computational Microbial Ecology research Group

(Helsinki Institute of Life Science, University of Helsinki) is looking for a postdoctoral researcher with background in neural network models . Our group  develops computational models, machine learning and