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: 280092639 Position: Postdoctoral Research Associate/Program on Science and Global Security Description: Princeton University's Program on Science and Global Security (SGS), based in the School of Public and
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, project partners. Dissemination of research findings in academic journals and (inter)national conferences, Lab supervision for PhD and undergraduate project students. For queries regarding applying online
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, and environmental engineers. Supervise MSc or PhD students. Qualifications: Ph.D. in Materials Science, Inorganic Chemistry, or a related field. Experience in the development of microporous materials
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to the application under CV/Resume. Required educations and experience Appropriate PhD in a related field. Preferred qualifications PhD, Computational Biology, Bioinformatics, Computer Science Training in statistics
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and BSc students as well as research assistants Qualifications: PhD in biotechnology, biochemistry, biology, chemistry, biophysics, or related area Experience with molecular biology and protein
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in Computer Science or related disciplines and has experience in cyber security and privacy research. They should have a good track record in relevant conferences and journals and has a track record in
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, Accessible Education Center, The Greek System, Mental Health Ambassadors (student group de-stigmatizing mental health services), and Peers In Pride (LGBTQ mentoring program) or the Pride Center. Time spent
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undergraduate students Assists in overseeing and performing tasks essential to laboratory function such as equipment maintenance, assisting with ordering and procurement, overall lab maintenance A Postdoctoral
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twobeautifulharbour-front locations in both Aalborg and Copenhagen. We have more than 250 employees and approximately 1,200 students across the twocities. Our research and study programmes arerooted in sustainability
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 14 days ago
latent spaces). Integrate dynamic, geometric, and biophysical features extracted from large MD simulations. Train and benchmark models on state-of-the-art datasets. Collaborate with PhD and Master students