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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 9 days ago
environmental sciences, with a particular interest in photocatalysis, nanomaterials, and pollution control technologies. He or she must be able to synthesize and characterize materials, master analytical
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characterize the physical properties of matter from both fundamental and applied perspectives, through theoretical and experimental approaches. The laboratory has strong international visibility in Soft Matter
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Healthcare Monitoring We welcome applications from those with expertise in or across these disciplines: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale
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program designed for students pursuing careers in the environmental and sustainability fields. Other existing strengths of the department include biomolecular chemistry and computation, materials synthesis
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environments that include solvent effects, thermal fluctuations, and the ingress of species such as chloride ions. First, DFT calculations will be used to characterize adsorption geometries, energetics, and
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(tooling and fixture). Experience in friction stir spot welding experiments (tooling and fixture) Experience in advanced characterization using techniques such as EBSD, XRD, TEM, etc. Experience in material
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plastics. Perform routine analytical procedures to characterize polymer modification and degradation. 20% Study community dynamics of various microbial strains that have been found to degrade plastics and
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(nucleosome) production and characterization methods with biochemical/biophysical techniques to enable the screening and characterization of small molecule binding and impact on chromatin structure and dynamics
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. Alison Feder, Freeman Hrabowski Scholar , based in the Department of Genome Sciences at the University of Washington in Seattle. The Feder lab investigates the dynamics of rapidly evolving populations
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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis