13 postdoctoral-machine-learning "https:" Postdoctoral positions at University of Minnesota
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developing cutting edge analytic tools for studying the genome transformation and genomic activities. 70% - The candidate will be mainly focusing on developing machine learning methods and/or AI algorithms
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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. Emphasis is placed on artificial intelligence/machine learning approaches applied to digital data and multi-omics data. Additional responsibilities include mentoring students, collaborating with faculty
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status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment is contingent upon
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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job A Postdoctoral Research Fellow position is available to work on research related to development of predictive display systems
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transportation systems and autonomous driving. • Strong understanding of generative AI, deep learning, and multimodal machine learning, with hands-on experience. • Excellent programming skills and proficiency with
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Department of Surgery, please visit https://med.umn.edu/surgery Pay and Benefits Pay Range: $63,480 - $71,748; depending on education/qualifications/experience Please visit the Benefits for Postdoctoral
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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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for Postdoctoral Candidates website for more information regarding benefit eligibility. Competitive wages, paid holidays, and generous time off Continuous learning opportunities through professional training
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis