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or applying AI/ML approaches to Earth, environmental, ecological, hydrological, or geospatial problems. Experience with programming in Python, GEE, or R, and working with modern scientific computing and/or AI
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, transcriptomics, metabolomics, phenomics, microbiome) for predictive modeling and biological interpretation. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data
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, biological sciences, computer science, machine learning) Demonstrated quantitative skills, including proficiency in programming (R and/or Python) Previous experience developing statistical methods or working
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experience in: Mammalian cell culture. High-performance computing environments. UNIX and Python and/or R. Reverse genetics systems or viral culture. Advanced statistical or computational data analysis
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of physical sciences, or in math, computer science, and electric engineering who have an interest in accelerator physics will also be considered. Strong programming skills. Proficiency in the Python programming
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, ligand GaMD). ● Experience building and interpreting Markov State Models or comparable approaches for identifying metastable states and conformational transitions. ● Proficiency in Python and
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, protein expression and purification Sequencing-based assays (RNA-seq, single-cell genomics, or related methods) Analyzing genomics datasets in Python or R Prior experience with Class I or Class II tetramer
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., RNA-seq, GWAS) Proficiency in R, Python, or similar computational tools Department Contact for Questions Questions regarding the position or application process can be directed to Dr. Tess Leuthner
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learning, or optimization Strong programming skills in Python and experience with scientific computing and machine-learning libraries Ability to work across experimental, robotic, and computational systems
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not expected. Experience with scientific programming, quantitative data analysis, numerical calculations and simulations using tools such as LabVIEW, Python, MATLAB, Mathematica, Origin, COMSOL