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Field
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. Basic Qualifications An ideal candidate will have a PhD in computational biology/bioinformatics/statistics/CS or another quantitative field, as well as superb programming (Python, shell scripting) and
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. Strong fundamentals in cancer biology and statistics are essential. Demonstrated proficiency in multiple programming languages such as Python/R, with experience in computational analysis of omics datasets
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software and research at www.cernaklab.com Required Qualifications* Candidate must have strong programming skills in Python, React, JavaScript and related languages. Expertise in one or more of the following
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biochemistry or analytical chemistry. An expert understanding of mass spectrometry, and its application for peptide and protein analytics in a dynamic research environment. A good understanding of Python
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of the relevant fields. Preferred skills: Previous experience in computational ecology and statistics. R or Python. Statistical analysis tools such as NIMBLE, JAGS or STAN. Familiarity with data processing, quality
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. · Expertise in working with large datasets and developing quantitative models. · Documented experience in programming languages such as Python, R, or VBA. · Proficiency in Excel and PowerPoint. · Experience
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externally (e.g., conferences) Required: PhD in Bioinformatics, Computer Science, Computational Biology, or related field Experience analyzing DNA sequencing data Proficiency in Python and/or R Familiarity
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and maintain Python and R codebase for transit routing and GIS operation automation. · Generate and write grant proposals with Dr. Liu on various topics. · Work with undergraduate and graduate students
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pursuing a bachelor's degree in the one of the relevant fields anticipated to be received by May 31, 2027. Preferred skills: Experience with fire management, landscape ecology, programming (e.g., R or Python
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field. Minimum of 2 years’ experience in cell culture, in vivo work, confocal microscopy or computational analysis of RNAseq in R/Python. Preferred Qualifications: Experience with primary cell culture