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Field
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(e.g., GWAS, eQTL, variant calling). Proficiency in computational tools such as Seurat, Scanpy, PLINK, LDSC and other relevant methods. Strong programming skills in Python and/or R for bioinformatics
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, Bioengineering, Computational Biology, Bioinformatics, or a closely related discipline. Demonstrated experience programming in Python or other major programming languages. Proven experience working in Linux
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such as JKSimMet, JKSimFloat, IES, or similar mineral processing software. Programming capability in Python, MATLAB, C++, VBA, or similar languages. Evidence of publication in reputable peer-reviewed
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(preferably infant or toddler) data Strong written and oral communication skills Strong analysis and programming skills (preferably in R and/or python) Ability to work independently and as part of a team
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complex materials systems. Advanced data analysis and scientific model development using Python or other scientific programming languages, including experience with automation, instrumentation control
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a strong computational focus. Applicants with interdisciplinary degrees are welcome. Strong data engineering skills in Python and/or R, preferably in building reusable data pipelines rather than one
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groups/populations. Preferred Qualifications PREFERRED QUALIFICATIONS: 1. Demonstrated experience with Linux/Unix environment, Python, and PyTorch. 2. Demonstrated experience with programmable network
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systems or autonomous AI frameworks · Solid foundation in computational biology · Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX) · Strong analytical, problem solving, and communication
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of study and possess working technical skills in python/MATLAB programming are preferred. The appointed position period is two years (1+1), with the first year being a probation year. Extension may be
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mentoring graduate and undergraduate students in laboratory research. Experience working at BSL2 or higher Experience in computational data analysis e.g. use of UNIX-based systems, R, Python, or similar tools