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to a cutting-edge computational research program in alignment with DIFFER's strategic mission. Use Generative AI in materials science for energy applications, which involves employing deep learning
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Postdoc Computational science for energy materials (X/F/M) Postdoc Computational science for energy materials (X/F/M) Published Deadline Location today 5 May Eindhoven Have you recently completed a
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, Nanoscience, Materials Science, Engineering or similar. A strong background in at least one of the following topics: 1) single-molecule microscopy/spectroscopy (e.g. smFRET), 2) DNA nanotechnology, 3) 2D
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please use the following link: https://www.rug.nl/research/ape Qualifications Candidates should have a PhD degree in materials science and engineering. A strong background in chemical vapor deposition and
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technology, applied physics and biomedical technology. Our fields of application include sustainable energy, process technology and materials science, nanotechnology and technical medicine. As part of a people
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of physicists, materials scientists, engineers and biochemists. We are looking for candidates that meet the following criteria: PhD degree in Physics, Biophysics, Nanoscience, Materials Science, Engineering or
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the Zernike Institute for Advanced Materials. The research group Photophysics and OptoElectronics is part of the Zernike Institute for Advanced Materials in the Faculty of Mathematics and Natural Sciences
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an advantage and should be mentioned in the motivation letter: PhD in science or engineering (e.g. geology, materials, chemical…) with experience in thermodynamic modeling. Experience as post-doc, staff member
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materials science and engineering. A strong background in chemical vapor deposition and materials physics will be a clear advantage. The successful candidate is expected to have an excellent academic record
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, Mechanical, Civil or Electrical Engineering, Materials Science or Applied Physics Experience with the fabrication of batteries Experience with electrochemical testing and a desire to learn about mechanical