12 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Duke University
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etc; Candidates with multidisciplinary backgrounds are welcome. · Strong skills in computational and data analytical methodology development and implementation; experience in machine learning and deep
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scientists (graduate students and technicians) · Flexing one’s intellectual muscles to think out-of-the box for this difficult-to-cure cancer Required Qualifications at this Level · PhD (or doctoral degree
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the university and with industry to translate discoveries into clinical proof of concept studies. Working with a team led by Drs. H. Kim Lyerly, Zachary Hartman and Josh Snyder, this program spans basic discovery
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of degree equivalency. Preferred Qualifications: Background in medical imaging, imaging simulation, and machine learning. Programming in Python, MATLAB, C, CUDA. Other Requirements: This position is hybrid
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., M.D., D.V.M.) Preferred Qualifications:. Detail-oriented, very well organized, and approach laboratory procedures with critical thinking. Strong initiative and eagerness to learn. Outstanding problem
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under Drs Ostrom and Patel’s mentorship. Be You. Requirements: The applicant should have a Ph.D. in computational biology, bioinformatics, computer science, statistics, genetic epidemiology, or a related
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to therapies and vaccines against human diseases. We are a team of highly interactive investigators that have expertise in immunology, molecular biology, virology, microbiology, structural biology, computational
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. · The appointment is viewed as preparatory for a full-time academic or research career. · The appointment is not part of a clinical research training program, unless research training under the supervision of a
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program. A doctoral degree or equivalent (Ph.D., ScD., DrPH, M.D., D.V.M., DDS etc) in Epidemiology, Biostatistics/Statistics, Bioinformatics, Genomics, or other relevant disciplines. Knowledge in the areas
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computational models of the immune response for multi-scale epidemic models. This position offers an excellent opportunity for recent graduates interested in applying quantitative and computational methods