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: Onboarding and Integration, and Innovation and Creative Capacity. The work will focus on investigating how hybrid work arrangements influence learning, collaboration, innovation, and organizational performance
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 3 months ago
of novel computational approaches at the intersection of semiempirical quantum chemistry, machine learning potentials, and implicit solvation models. Requirements Experience in computational chemistry
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excellent knowledge of English (written and spoken). Be able to work well and communicate expert knowledge in an interdisciplinary team. Have high self-motivation to learn. Have the ability of working
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knowledge of English, 5. publication achievements (preferably international), 6. teamwork skills – openness, tolerance, good communication skills, 7. Candidates must meet the criteria specified in the terms
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Research Engineer or Postdoctoral Researcher (m/f/d) in the field of Engineering Physics or Material
engineering A completed Master’s degree or PhD in Engineering Physics, Materials Science, Engineering or a similar discipline The ability to work independently, a willingness to learn, and a team spirit Good
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comfortable in an international academic setting, and you can communicate clearly in English, both in writing and orally, when working with colleagues and students. You are ready to engage constructively in
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, our Palmetto College campuses in Salkehatchie, Union, Lancaster and Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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, or (applied) physics. Excellent lab-experiences Advanced mathematical, analytical skills, and programming skills, e.g., in Python. A keen interest in interdisciplinary research. An excellent command of English
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive