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and data science. This cycle's focus is on AI-powered data analysis to advance hypothesis-driven research related to addiction. Computational analysis of previously collected data can increase the speed
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-powered data analysis to advance hypothesis-driven research related to addiction. Computational analysis of previously collected data can increase the speed and efficiency of life sciences research
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energy-conversion testbeds, evaluating activity, selectivity, stability, and energy efficiency under realistic operating conditions Develop reaction and reactor models and conduct process analysis
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, problem-solving, collaboration — using a pre/post design with approximately 300 co-op students, paired with structured employer interviews and a library of just-in-time learning interventions
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Qualifications: Ph.D. in Chemistry, Computer Science or equivalent In-depth knowledge and hands-on experience in MD simulation and molecular modeling Experience in processing and analyzing protein structure and
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dynamometry. Develop and maintain analysis pipelines and ensure data quality across sessions. Prepare manuscripts, conference abstracts, and grant applications as first author and contributing author. Develop
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in cortical circuits mediating defensive responding. Responsibilities will include rodent colony management, stereotaxic surgery, behavior testing, fiber photometry protocols and data analysis, brain
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, Chemistry, Materials Science, or a closely related field Knowledge of heterogeneous catalysis, reaction kinetics, thermodynamics, and structure–reactivity relationships Ability to design and operate catalytic
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conditions. Bioinformatics skills relevant to genomics-driven natural products discovery, including genome mining, biosynthetic gene cluster analysis, sequence alignment, and annotation. Strong quantitative
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and computation, involving the analysis of feedback controllers in systems and synthetic biology, the analysis of algorithms, and merging of ML and mechanistic approaches to deal with context effects