93 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "J" "U.S" research jobs at Duke University
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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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● Demonstrated proficiency with statistical programs such as R, SAS, and/or STATA ● Ability to work independently and as part of a research team ● Commitment to academic diversity, equity, and inclusion Submit a
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is highly desirable. A strong record of research publications is preferred. SKILLS: Strong programming skills (e.g., Python, C/C++, CUDA or similar); Background in algorithms for physical design
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partnerships with local Duke EM partners. •Strong quantitative skills in the areas of biostatistics, bioinformatics, and/or epidemiology. •Strong programming skills (R or Python preferred). •Knowledge
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● Strong background in observational or computational cosmology, large-scale structure, weak lensing or image processing ● Proven experience in scientific programming in Python and/or C++ ● Deep familiarity
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functions as a vaccine development biotechnology enterprise, embedded within a top university. As a trainee within our mentoring program, co-directed by Drs. Blasi and Williams, you will interact with highly
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, deep learning Computational genomics, network modeling, spatiotemporal/functional data analysis, time-series Strong programming in R and/or Python; best practices in reproducible research Excellent
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and revision Educational Requirements: Work requires a PhD (bio)statistics, econometrics, data science, or related field. Experience/Skills Required: A background in commonly used programming
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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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, deep learning, or statistical modeling. Demonstrated experience working with clinical, digital health, or related biomedical data. Proficiency in Python, R, or other scientific programming languages