Sort by
Refine Your Search
-
progression, machine learning. You will collaborate locally and internationally with groups in both theory and experiment. You will disseminate your findings by publishing in scientific journals and presenting
-
groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
-
synchrotron infrastructure tools for ex-situ and in-situ experiments to acquire essential information regarding the microstructure and the physical mechanisms involved during thermomechanical loading
-
PhD students and postdocs. Research at the DSAI ranges from foundational methods in machine learning (e.g., optimization, bandits and reinforcement learning) to application domains in biophysics
-
of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial intelligence in general. The focus is broadly upon the development
-
/or reactor physics Documented knowledge/experience in machine learning What you will do As a PhD student, you will have the opportunity to shape your research project while receiving guidance and
-
passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
-
physics and strongly interacting systems? As a PhD student in theoretical nuclear physics, you will have the opportunity to explore deep questions about the origin and properties of atomic nuclei and the
-
the reach of conventional computers. These challenges span fields such as optimization, quantum chemistry, materials science, and machine learning. Building a quantum computer requires a multidisciplinary
-
learning-based design methodologies. We are seeking a motivated individual with a strong interest in deep learning and RF circuit design. Your work will involve exploring deep learning techniques for silicon