11 parallel-programming "SciLifeLab " Postdoctoral positions at University of Florida
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mechanisms underlying this developmental programming. Current studies focus on how bile acid signaling during early postnatal life establishes adipose tissue architecture and function, including effects on
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) mathematics, physics, or a related STEM fields. Strong programming and data analysis skills (e.g., Python, R) Solid understanding of machine learning, deep learning, and data modeling techniques Job
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quantitative research and analyzing experimental, observational, or engineering data. Proficiency in at least one analytical or programming environment, such as Python, R, MATLAB, or equivalent software
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related quantitative STEM field. • Strong programming skills in Python and/or R; experience with SQL and Linux/Unix is desirable. • Demonstrated research experience in biomedical informatics, artificial
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in biology or related fields. Preferred Qualifications: A strong background in programming (e.g., Python or R or C++) is essential. Research experience utilizing skills of programming, statistics, and
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or more of the following areas is desirable: Statistical analysis (e.g., SPSS or R) Programming experience (e.g., MATLAB or Python) Demonstrated trajectory toward applying for an NIH K-award within
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learning to quantum chemistry and a programming background in Python and C++. Preferred: Familiarity with wavefunction-based quantum chemistry. Experience and interest in fundamental studies of chemical
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ability to work effectively both independently and as part of a collaborative research team. Preferred: Familiarity with relevant software packages and/or programming languages, such as Bruker TopSpin
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will be relevant. Experience with AFNI/SUMA, SPM, FSL, Freesurfer, UNIX/LINUX computational environments and/or programming skills (MATLAB, R, C++; JAVA, Python) is desirable, but not required
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similar, with a focus on large-scale, quantitative research. · Strong programming skills in Python and experience with scientific computing, data processing, and reproducible research workflows