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of the appointment. Demonstrated proficiency in data analysis, statistical modeling, and data visualization using R, Python, or comparable analytical software. Evidence of effective scientific communication through
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, R and/or Python) for transcriptomic analysis and data integration. Experience in bacteriology and BSL-2 practices, including Salmonella or other enteric pathogen culture, serotyping/purity
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to damage or loss data; Experience with programming (Python or R) and working with large datasets; Ability to design and evaluate field experiments or quasi‑experimental studies is an asset; Strong written
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Computational Postdoctoral Researcher - Bacterial Genomics, Transcriptomics, and Single-Cell Genomic
. • Strong computational and programming skills (Python, R, Linux, and high-performance computing environments). • Demonstrated experience in genomics data analysis, evidenced by peer-reviewed publications
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reports, or research manuscripts. Preferred Qualifications ● Strong coding/programming skill (e.g., Python, C++, etc) is a plus. ● Experience with USRP prototypes is a plus. ● Experience with Nvidia
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an advantage skills in electronics, e.g., RF electronics, MRI coil design, gradient coil design, etc. programming skills with MATLAB, C/C++ or Python skills and experience in manufacturing methods, such as 3-D
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related field; Strong publication record in relevant venues; Solid programming skills in Python and a modern deep-learning framework Excellent communication skills in spoken and written English; Self
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inference methods (e.g., fixed effects, IV, DiD) Advanced regression techniques - Strong programming and analytical skills (R, Stata, Python, Mplus, or similar). - Evidence of research productivity or strong
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related area. Demonstrated research experience with large language models, generative AI, agentic AI, or related foundation model technologies. Strong programming skills in Python and experience with modern
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Strong programming skills (e.g., C/C++, Python, MATLAB) and familiarity with scientific software development best practices. Evidence of scholarly productivity and effective collaboration in