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Field
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proficiency. Preferred Qualifications: Prior knowledge and experience in at least three of the following: working with high-performance and cloud computing, programming in Python, data analysis and
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programming languages such as C and Python Proficiency in deep learning frameworks such as Pytorch and Tensorflow Knowledge in imaging and computing device and equipment Good written and oral
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§ Clinical metadata harmonization across cohorts § Integration of clinical and multi-omic microbiome data. § Experience with Python/R and cloud computing preferred Ideal candidate: A computational
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Proficiency in scientific computation in C/C++ and Matlab/Python or similar languages Good communication and writing skills The College of Science seeks a diverse and inclusive workforce and is committed
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. Additional Skills (Preferred): Experience with environmental data analysis (R, Python, multivariate statistics). Experience in GIS and ecological mapping. Strong scientific writing skills and command
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communication, or signal processing. Proficiency in programming languages like Python, MATLAB, or C++, and experience with AI/ML frameworks like TensorFlow, PyTorch, or scikit-learn. A proven track record of
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support systems. To be successful for this position, you’ll have: Demonstrated experience in the application of LLM. Demonstrated experience in programming in Python. Ability to learn new technology and new
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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for educational uses Good hands-on experience in programming, e.g., C/C++/C#, CUDA, Python, and scripting Track record in research and publication particularly in education Strong knowledge and hands-on experience
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qualitative and quantitative data analysis. Proficiency in programming (e.g., JavaScript, Python, React for iOS/Android) is important. Additional strengths would include experience with physiological sensing