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Field
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-omic data integration. Computational skills for data analysis (e.g., Python/R, workflow tools, and reproducible research practices). Prior work related to neurodevelopmental disorders, regulatory
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plasma-material interactions, including models for sputtering, impurity transport, and sheath physics. Programming experience in Python, MATLAB, Fortran, C++ and other science relevant languages. Ability
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/instruction tuning methods. * Understanding of Reinforcement Learning (RL) principles, multi-objective optimization, or active learning/Bayesian optimization. * Strong programming capabilities in Python
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pipelines (e.g., handling transcriptomic or scRNA-seq datasets using R or Python). Special Instructions to Applicants: In order to be considered, you must upload your cover letter and resume. What We Offer
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. Familiarity with NGS data analysis and command-line bioinformatics (e.g., CRISPResso, Python or R). Experience mentoring students or technicians and coordinating lab operations. Special Instructions
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software that allow time-evolving models of complex systems to be calibrated against sparse, indirect, and uncertain observations. The group develops and maintains an open-source Python framework
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-reviewed journals. Attend conferences to disseminate the ideas. Share the Python codes in a well-maintained GitHub repository for public use. Make R or Python package for widespread use of the methods. May
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for the collection of in-situ mechanical testing data. Knowledge of scientific software in python (numpy, scipy, pandas) or related data analysis suite. Record of productive and creative research as demonstrated by
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, MCNP, Serpent, OpenMC, and/or MOOSE and familiarity with reactor systems and concepts. Programming/scripting experience including the use of one or more of C/C++, Python, Fortran, Git, and CMake an asset
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communication skills, including preparation of manuscripts and scientific presentations. Experience with programming or quantitative analysis tools (e.g., MATLAB, Python, R, Spike2, or related platforms) is desirable