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and other machine learning models (especially neural network models, time-series models) and coding in python and R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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applying data science principles and techniques to biomedical datasets. Applicants should be comfortable using coding skills such as SQL, R, and Python to extract data from databases, clean it, and analyze
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or MD/PhD Strong programming skills, preferably in R and/or Python Previous expertise and/or interest in single-cell sequencing technologies, bioinformatics, spatial analyses, and generative AI is desired
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mechanisms of neuromodulation. Discover mechanisms how diverse ligands targeting GPCRs or diverse receptors elicit distinct phenotypic responses. Relevant publications Lobingier B and Hüttenhain R, et al
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model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction
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or gene editing by CRISPR-Cas9 is required. Experience with flow cytometry and/or mass cytometry analysis is desirable. Interest in learning new technologies is mandatory. Basic R programming skills
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an individual with strong statistical and computing backgrounds. Successful applicants should have a Ph.D. degree in epidemiology (or biostatistics or a related field). Strong programming skills in R are required
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-grants/stanford-heal-t90-r... (link is external) How to Submit Application Materials: Submit your application here: https://redcap.stanford.edu/surveys/?s=C4NC3KNR8EJYPANK (link is external) Does
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latent variable models (especially factor analysis, item response theory, and growth modeling) and coding in R. Strong collaborative skills and ability to work well in a complex, multidisciplinary
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Expertise and hands-on experience using LLMs, Machine Learning, Deep Learning frameworks, Natural Language Processing, etc. Proficient programming skills in Python and R and working knowledge in SQL Strong