11 data-mining Postdoctoral positions at Massachusetts Institute of Technology in United-States
-
Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 16 hours ago
publishing research findings. Additional experience with autonomous data collection systems (e.g., profiling floats), Lagrangian analysis, adjoint modeling, AI/ML techniques, and High-Performance Computing
-
; expertise in construction materials science, industrial byproduct utilization, or technology adoption; strong quantitative skills and experience synthesizing technical and market data and material flow
-
combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO) and General Observer (GO) programs
-
for Information and Decision Systems (LIDS) and the MIT Sloan School of Management in collaboration with The Social and Ethical Responsibilities of Computing (SERC) invite applications for a postdoctoral associate
-
the forward and inverse problems involved. Additional information about AGAGE, CS3, and MIT Atmospheric Chemistry, is available at https://agage.mit.edu, https://cs3.mit.edu, and https://rprinn.mit.edu. Job
-
Posting Description POSTDOCTORAL ASSOCIATE, Laboratory for Information and Decision Systems (LIDS ) and Sloan School of Management in collaboration with The Social and Ethical Responsibilities
-
Jet Propulsion Laboratory, focused on developing a next-generation, GPU-based climate model that learns physics from data to improve the accuracy of its projections. Will collaborate with oceanographers
-
materials should include a cover letter, CV, and the names and contact information of three references. These materials should be submitted as a single PDF in the resume field of the application. 8/21/2025
-
that includes a complete list of publications; and the names and contact information of three references. 9/2/2025
-
qualifications and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, pregnancy, religion, disability, age, genetic information, veteran status, or national