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Field
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD (or foreign equivalent) in Physics, Electrical Engineering, Material Science or closely related field in hand
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Employment 0.8 - 1.0 FTE Gross monthly salary € 4,537 - € 6,209 Required background PhD Organizational unit Faculty of Social Sciences Application deadline 21 August 2025 Apply now Do you want
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effectively with a multidisciplinary team spanning engineering, biology, and computational sciences. Required Qualifications Minimum of a PhD or Doctorate in Chemical Engineering, Bioengineering, Biophysics
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PhD or Doctorate in PhD or Doctorate in public health, psychology, social science. Minimum of 1 year of experience. Experience conducting quantitative, qualitative, and mixed-methods data analysis
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University is known worldwide for its high academic quality and societal impact. The Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and about 40 % of all
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primarily on structural analysis of protein complexes and will best suit a candidate with a PhD and relevant experience in protein biochemistry and structural biology, specifically cryo-EM/ET. The second post
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
pharmacy practice, education, research, and public service. Position Summary The Structural Genomics Consortium (SGC) at UNC-Chapel Hill is seeking a highly motivated researcher to join our team as part of
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A postdoc position in the Torben Heick Jensen lab, Aarhus University, Denmark: Mammalian Nuclear ...
to molecular biology and bioinformatics approaches. Collaboration with structural biologists is possible. Your profile Applicants should hold a PhD in Molecular Biology, Biochemistry, Cell Biology, Genomics
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the research structure using integrated management approaches in pursuit of capacity building/strengthening and the preservation of scientific rigor in research studies. Support if required, the development
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to self-organize into complex structures. Our approach is to develop sophisticated mathematical models – informed by state-of-the-art biological knowledge and experimental data – to understand