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using AI tools, Python, MATLAB, R, or similar tools Position Details Expected start date: Summer/Fall 2026 (flexible) Competitive salary and benefits Location: University Park, Pennsylvania Penn State
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populations). Proficiency in R, Python, and SQL. Certifications/Licenses Required Knowledge, Skills, and Abilities Leadership role on funded grant(s). Preferred Qualifications Experience with EHR data such as
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, GraphPad Prism, Python/R, or related tools. Experience or interest in cancer pharmacology, immuno-oncology, natural killer cell biology, drug discovery and development, translational therapeutics, blood
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for advanced data analysis and/or experimental control (e.g., Python, SPEC, MATLAB, etc.) Experience with relevant sample preparation and lab-based analyses (e.g., SEM, Raman) Experience with or interest in
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(HPC), or large-scale data analysis. Experience in applying AI/ML techniques to hydrological and Earth sciences. Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C
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, or equivalent) conferred by the start date. 2. Proficiency in R/Python. 3. Experience with spatial proteomic/transcriptomic data analysis. 4. Growth mindset and motivation to advance our understanding of breast
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, epigenomics, or transcriptomics Basic experience in computational biology or familiarity with bioinformatics tools (e.g., data analysis pipelines, R/Python, or multi-omic data interpretation) is advantageous
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, completed prior to the start date; Experience working with the scientific Python ecosystem (NumPy, SciPy, Matplotlib, etc.) and with databases (SQL, etc); Experience with collaborative version control systems
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analysis (16S rRNA sequencing, QIIME2, DADA2, R/Python) and experience with rodent models of inflammation or dietary intervention studies. Applicants should be highly motivated and creative, have excellent
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | 2 months ago
one of: Python, R, C/C++, or Perl; proficient with Unix/Linux. •Experience with deep learning, statistical modeling, or AI applications to biological data. •Familiarity with transcriptomic technologies