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, United States of America [map ] Subject Areas: Computer Science Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Appl Deadline: none (posted 2025/08
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multiple affiliations. Be You. Minimum Requirements: The Postdoctoral Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM). Candidates with non-US degrees may be required to provide proof
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collaboration between Dr. Warren Grill and Dr. Angel Peterchev at Duke University and Dr. Axel Thielscher at the Danish Research Center for Magnetic Resonance and Technical University of Denmark. This work is
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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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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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analysis using appropriate machine learning techniques and contribute to the writing of technical papers and research proposals. Duke is an Equal Opportunity Employer committed to providing employment
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quantitative methods and excited about discovering physical principles of biological organization. Minimum Requirements: PhD in a scientific disciplines, ideally Biology, Bioengineering, Physics or Math
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to mentor students, teach/train other researchers in LCA tools, and develop independent research projects as desired. The successful applicant will possess a PhD in chemical engineering, chemistry
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ecosystem; contribute to the mentoring of trainees within the lab; publish peer reviewed manuscripts and contribute to funding proposals. Educational Requirements • PhD in Chemistry, Bioinformatics