70 fully-funded-phd-program-computer-science-eth Postdoctoral positions at Duke University
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. Candidates with backgrounds in epidemiology, environmental health, biostatistics, or exposure science are encouraged to apply. Position Details: This position will support an NIH-funded study leveraging
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Training Program. This position will be funded by our NIDDK T32. Eligibility: U.S. citizenship or permanent residency required PhD applicants must have been awarded their degree or anticipated prior to June
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, Duke University Biology Department to study how archaeal microbial communities respond to stress in hypersaline environments. A PhD in computational and/or experimental biology is required in fields
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on the candidate’s interests and experience. Requirements: Ideal candidates should hold a PhD in the area of data science, computer science, bioinformatics, structural biology, or a related area, and have experience
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-of-the-art IT infrastructure. Ideal candidates should hold a PhD in the area of Computer Science or Electrical and Computer Engineering and have strong programming skills including Python. Past work in
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of Civil and Environmental Engineering / Chaney Lab: Perform the core of the proposed research activities including processing the remotely sensed LST to compute the spatial statistics, run the HydroBlocks
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Health Program Program Type: Postdoctoral Location: Durham, North Carolina 27708, United States of America [map ] Subject Areas: Biology Mechanical Engineering Public Health Biomedical Engineering
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Preferred Qualifications: A PhD or MD/PhD (or equivalent) in biological sciences (cell & developmental biology or a related field) which was awarded not more than 18 months ago. Evidence of successful
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appointment is generall y preparatory for a full time academic orresearch career.The appointment is not part of a clinical training program, unlessresearch training und er the supervision of a senior mentor is
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transcranial electric and magnetic stimulation (TES and TMS) on an NIH-funded R01 project to develop and disseminate computational neuron models and their integration with TES and TMS electric field simulations