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Postdoctoral & Senior Researcher Positions in Computational Chemistry Center for Advanced Reaction Dynamics (CARD), Institute for Basic Science (IBS) Location: KAIST Campus, Daejeon, South Korea
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PostDoc (with the option for a one-year extension), in the Computational Chemistry group at Boehringer Ingelheim, Biberach, Germany. For this PostDoc grant via opnMe , we invite you to submit your research
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chemistry or physics, or related area. Preferred qualifications The ideal candidate has experience running electronic structure calculations and has expertise with a variety of computational approaches
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: Postdoctoral Position Location: Taipei, Taipei, Taiwan Subject Areas: Physics / Biology , Biophysics , Chemistry , Computational physics , Condensed Matter Physics , Machine Learning , Nanotechnology
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Graduation in the area of Computational Chemistry, Material Science, Chemistry, Artificial Intelligence or Physics within 3 years; Have strong research and innovation abilities, and be able to engage in full
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
of modern chemistry, and our research program is one of the strongest in the country with more than 300 publications annually and over 100 patents filed within the last 10 years. Our research has led to a
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of nitrides, along with Raman spectroscopy measurements. This programme of research will be conducted in collaboration with researchers from the University of Bayreuth (Germany), the Commissariat à l’Énergie
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track
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About the role Applications are invited for a Postdoctoral Research Associate in Chemistry to work under the supervision of Professor Andrew Baldwin for a period of up to one year. The project is
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying