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
-
. The project will focus on research and development of advanced machine learning and deep learning algorithms to analyze large quantities of multimodal images and data arising from the Advanced Plant Phenotyping
-
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
-
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
-
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