Sort by
Refine Your Search
-
mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
-
outside Duke University. Preferred qualifications: PhD (completed in the last 1-5 years or PhD candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science
-
genomics, metabolomics, or microbiome analysis Computer science, particularly machine learning, artificial intelligence, data science, or computational biology Mathematics or statistics, with experience in
-
projects currently under grant support. Required Qualifications at this Level Education/Training PhD Duke University is an Affirmative Action/Equal Opportunity Employer committed to providing employment
-
automation tools for academic and translational applications. Required Qualifications at this Level Education/Training PhD Duke University is an Affirmative Action/Equal Opportunity Employer committed
-
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
-
regarding all facets of the Postdoctoral Appointee's research activities. Must hold a PhD Duke University is an Affirmative Action/Equal Opportunity Employer committed to providing employment opportunity
-
team to unravel the mysteries of membrane ion and lipid transport and their roles in various diseases. Minimum Requirements: PhD in biochemistry, biophysics or cell biology Preferred Qualifications
-
independent research activities under the guidance of a faculty mentor in preparation for a full time academic or research career DEFINITION The Postdoctoral Appointee holds a PhD or equivalent doctorate (el gl
-
earned a PhD in Ecology, Marine sciences, or a related field by the time of appointment. Expertise in marine conservation science, coastal fish ecology, salt marsh and seagrass ecology and assessment