Sort by
Refine Your Search
-
science, computational sciences, or mathematics is preferred. Knowledge required includes machine learning and statistical methods, proficiency in programming with C, C++, and Python, familiarity with Cloud systems and
-
-time university employees are available. Qualifications The successful candidate should demonstrate strong research experience in multiphysics modeling and/or machine learning in food processing and
-
engineering, applied mathematics, physics, or related field with an evidence of understanding nuclear nonproliferation and/or the nuclear fuel cycle. Experience applying machine learning techniques to problems
-
Professor Alan Tennant on novel quantum magnetic phases, out-of-equilibrium phenomena, and application of machine learning. Candidates who have experience in neutron scattering, materials characterization and
-
the laboratory Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs Ability to acquire data and control
-
Experience with multi-physics simulations on high performance computing (HPC) and machine learning (ML) Experience working in a multi-disciplinary research environment Demonstrated written and oral
-
completed in the last five years. Hands-on experience with machine learning, process modelling, and industrial data acquisition systems is very valuable. This position may also require access to technology
-
the Geospatial Science and Human Security Division (GSHSD) at ORNL. The group performs artificial intelligence, computer vision, and federated learning research initiatives, with emphasis on large scale geospatial
-
environments with machine learning, and complex systems simulation research. Job Research Professional Primary Location US-Tennessee-knoxville Organization College Of Nursing Schedule Full-time Campus/Institute
-
Requisition Id 12983 Overview: We are seeking a Postdoctoral Research Associate who will focus on developing and applying machine learning algorithms relevant to autonomous experimental laboratories