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on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would
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Institute (https://cse.umn.edu/aiclimate). The role involves building knowledge-guided machine learning (KGML) models for sustainable agricultural practices, developing AI-ready benchmark datasets, and
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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wages, paid holidays, and generous time off Continuous learning opportunities through professional training Medical, dental, and pharmacy plans Healthcare and dependent care flexible spending accounts
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computational modeling of behavior to identify the underlying circuit computations. Current projects in our laboratory emphasize task-structured behavior assays, including set shifting, reversal learning
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with specialized equipment and software in a way that ensures success and appropriate care of the equipment. • A strong desire for growth, allowing for quick learning and active seeking of feedback
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treatments and specific kinase inhibitors in clinical settings, ultimately aiming to enhance outcomes for cancer patients through the discovery of new druggable targets. Learn more about us at lun.umn.edu, or
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to teach team members how neural network simulations work Author, co-author, and otherwise collaborate on reports, conference papers and journal articles. (25%) Communicate with PIs about model development
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research in national and international meetings in oral and poster format by preparing abstracts and slides for presentation. - Instruct students, fellows and other inexperienced professional persons in
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benefits package that includes: Competitive wages, paid holidays, and generous time off Continuous learning opportunities through professional training and degree-seeking programs supported by the Regents