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Field
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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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spintronics, or cavity spintronics. Proficiency in scientific software development (e.g., Python, COMSOL, HFSS). Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
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techniques. ● Proficiency in instrument control, data acquisition, and data analysis using tools such as LabView, Python or MATLAB. ● A record of peer-reviewed publications and demonstrated ability
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(for example Python, bash, or an equivalent language). ● At least one peer-reviewed publication in a scientific journal. Preferred Qualifications: ● Experience with free-energy or phase-boundary
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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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/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