Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
metabolism (https://ducker.biochem.utah.edu/ ). The candidate will perform technical procedures in a research laboratory under minimal supervision by applying standard scientific techniques and analytical
-
construction. The technicians are expected to work closely with the PI to carry out specific experimental tasks and generate publication-quality data. They will also assist with maintaining lab supplies and
-
models, and develop the next-generation multi-physics simulation environment for hypersonics. To support these goals, the University seeks applicants with significant experience in computational fluid
-
, Multiphase flows and complex fluids, Turbulence and transition, Newtonian and non-Newtonian flows, Reactive flows, Microfluidics , Heat transfer, Experimental techniques. Dynamics and Control Systems
-
.) and the Saint Anthony Falls Laboratory (https://cse.umn.edu/safl), a unique and globally recognized facility for pioneering research in experimental and computational fluid mechanics, hydrology
-
physics, as well as cosmology. Specific areas of interest include: Heavy flavor physics, CP-violation, and perturbative QCD. Relativistic kinetic theory, fluid dynamics, and high-energy nuclear collisions
-
supervision of Dr. Miha Založnik and Prof. Hervé Combeau, experts on solidification, and Dr. Jean-Sébastien Kroll-Rabotin, expert on multiphase fluid dynamics. She/he will be part of the Solidification group
-
the couplings between solid and fluid phases, as well as the chemical components of both, specifically electrically charged GAGs combined with collagen as well as physiological ions. The fluid-structure
-
doctoral research institution, the Bass School fosters the fluid movement between traditional disciplines and cutting-edge experimental investigation of emerging technologies. We prepare students with
-
experimental validations. Training an AI agent using both generated data and engineers’ evaluations of design trials. Deploying the trained model in increasingly complex representative environments