Sort by
Refine Your Search
-
modeling with AI/machine learning frameworks is a plus Department Unit/Website: www.ameslab.gov Proposed Start Date: October 1, 2025 Proposed End Date or Length of Term: September 30, 2026 Number of Months
-
engineering, or a related field. · Strong background in machine learning or data analytics and hands-on experience handling big data. · At least one year of research experience in transportation
-
research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems for viral agents. Emphasis is placed on determining
-
on reliability, security, and resilience of electric power systems and microgrids and stability analysis and Scientific Machine Learning (SciML) for microgrid applications. The successful candidate will be
-
U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 13 hours ago
of innovative technology solutions. Assignments will require technical and economic research involving data analysis, regulatory analysis, and modeling. You will learn by assisting with producing this research
-
innovative research, UTA is designated as a Carnegie R-1 “Very High Research Activity” institution. UTA ranks No. 4 nationally in Military Times’ annual “Best for Vets: Colleges” list and is among the top 30
-
a position combining project management, machine-learning model development, data management/analysis, and manuscript writing for publication. Preference will be given to applicants with (1
-
designated as a Carnegie R-1 “Very High Research Activity” institution. UTA ranks No. 4 nationally in Military Times’ annual “Best for Vets: Colleges” list and is among the top 30 performers nationwide
-
National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 26 days ago
. Description: The post-doctoral fellow will apply machine learning and/or other data science techniques to conduct scientific research, improve remote sensing data products and analysis, integrate machine
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 5 hours ago
observations and model-based data. Apply machine learning techniques to optimize factor weightings and reveal critical wildfire drivers. Validate models using multi-source wildfire data (MODIS, VIIRS, and NOAA