14 machine-learning-"https:"-"https:"-"https:"-"https:"-"Simons-Foundation" Fellowship positions at University of Texas at Austin
Sort by
Refine Your Search
-
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
-
one or more of the following areas: (1) modeling of infectious disease dynamics, (2) statistics, machine learning, and AI, or (3) operations research and optimization. Preference will be given
-
and childcare expenses Robust free training access through LinkedIn Learning plus professional conference opportunities Tuition assistance Expansive employee discount program including athletic tickets
-
have been received within the last three years, 1 year of experience with machine learning, natural language processing, AI tools and frameworks, data integration, and/or explainable AI. Proficiency in
-
has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs
-
or the Head of Book and Paper Conservation. The fellowship offers an opportunity to acquire advanced training while working in a highly collaborative environment that includes six conservators (book, paper