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Lawrence Livermore National Laboratory

Livermore, California

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Lawrence Livermore National Laboratory

Livermore, California

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Lawrence Livermore National Laboratory

Livermore, California

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Lawrence Livermore National Laboratory

Livermore, California

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Lawrence Livermore National Laboratory

Livermore, California

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Lawrence Berkeley National Laboratory

Berkeley, California

Inspira Health

Vineland, New Jersey

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Lawrence Livermore National Laboratory

Livermore, California

University of Kaiserslautern-Landau (RPTU)

Rhineland-Palatinate, Germany

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Lawrence Livermore National Laboratory

Livermore, California

Stony Brook University

Stony Brook, New York

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Lawrence Livermore National Laboratory

Livermore, California

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University of Massachusetts

Amherst, Massachusetts

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AIP Publishing

Melville, New York

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AIP Publishing

Melville , New York

University of Missouri - Columbia

Columbia, Missouri

Lawrence Livermore National Laboratory Logo
Lawrence Livermore National Laboratory

Livermore, California

University of Missouri - Columbia

Columbia, Missouri

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Lawrence Livermore National Laboratory

Livermore, California

Lawrence Livermore National Laboratory Logo
Lawrence Livermore National Laboratory

Livermore, California

Lawrence Livermore National Laboratory Logo
Lawrence Livermore National Laboratory

Livermore, California

Lawrence Livermore National Laboratory Logo
Lawrence Livermore National Laboratory

Livermore, California

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Professor or Assistant Professor (Tenure Track) of Materials Modeling
The Department of Materials (www.mat.ethz.ch) at ETH Zurich invites applications for the above-mentioned position. Successful candidates must have demonstrated research excellence in the development of methods for theory- or computer-based materials modeling applied to solve materials problems at the cutting edge of research. The demonstration of an outstanding research track record and a clear vision for future developments, preferably including the application of machine learning or artificial intelligence in materials science, are essential.Experience with static simulation methods (such as Monte Carlo) or dynamic simulations (atomistic or molecular) are desirable. This is to be combined with machine learning or coarse-


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