19 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Fellowship positions at University of Texas at Austin
Sort by
Refine Your Search
-
computational cluster and/or high-performance computer with Linux OS. Excellent oral and written communication skills and a collaborative working mindset. Strong publication record in closely related research
-
areas such as data analysis, statistical modeling, machine learning, numerical modeling, or remote sensing Preferred Qualifications A general understanding of ecosystem modeling or ocean circulation
-
of Mathematics. https://www.ma.utexas.edu/ Each R.H. Bing Fellow holds an Instructorship in the Department of Mathematics with a teaching load of one course per long semester. The combined Instructorship
-
coverage Retirement contributions Paid vacation and sick time Paid holidays Please visit our Human Resources website (https://hr.utexas.edu/prospective/benefits ) to learn more about the total benefits
-
found here: https://www.jsg.utexas.edu/people/postdoctoral-fellows-programs/unit-level-postdoctoral-fellowships/ . For questions on these positions, please contact Dr. Chenguang Sun at csun@jsg.utexas.edu
-
microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and
-
Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
or parallel reactors Collaborate with computational scientists to integrate machine-learning models for closed-loop materials discovery Collaborate with companion postdocs on functional materials
-
the last three years. Solid experience with AI/machine learning methodologies, particularly those applicable to network optimization. Proven ability in programming and familiarity with network simulation
-
intelligence that coordinates the entire experimental ecosystem. The Postdoctoral Fellow will develop agentic AI models and frameworks for intelligent experimental workflows, which couple machine learning, real
-
in this position will conduct/lead applied as well as fundamental research in physics-informed Artificial Intelligence (AI) and Machine Learning (ML) methodologies enabling digital twin functionalities