81 machine-learning-phd-in-netherland Postdoctoral positions at University of Minnesota
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• PhD, DDS, DVM, JD, MD or equivalent is required. • Demonstrated motivation and initiative in completing tasks and contributing to team goals • Fluent in English with strong verbal and written
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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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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job As a postdoctoral associate, you will work under the mentorship of Mark Osborn, PhD, contributing to cutting-edge research in
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safety standards and adhere to University of Minnesota policies. Assist other lab/community members, as needed. Qualifications Required Qualifications PhD in Environmental Engineering, Chemistry, Chemical
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conferences (20%) Qualifications Required Qualifications: - A B.S. degree related to a biomedical field. Preferred Qualifications: - Advanced degree (PhD) in biomedical sciences or related field(s) - Experience
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stipend level Please visit the Benefits for Postdoctoral Candidates website for more information regarding benefit eligibility. Competitive wages, paid holidays, and generous time off Continuous learning
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conducting clinical or preclinical proof-of-concept studies Preferred: Experience in physiological signal processing and the application of machine learning to biomedical data Background in computational
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Qualifications: PhD in plant science, horticulture, agronomy, or a related field Pay and Benefits Pay Range: $61,008 - $62,000; depending on education/qualifications/experience Please visit the Benefits
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. The candidate must have a track record of reliability and good verbal and written communication skills. This is an ideal position for a recent graduate with an PhD, MD/PhD degree in related fields, but not
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. The role offers exceptional opportunities for professional development, including learning the latest advancements in large language models, translating these innovations to genomic applications, and leading