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on the development, ecology, evolution, and physiology of marine organisms worldwide from the intertidal zone to the deep sea. Position Summary The Oregon Institute of Marine Biology seeks qualified applicants for its
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following areas: 1) statistical genetics/genomics/omics, or 2) deep learning/AI. Most importantly, we value candidates who demonstrate both the ability and drive to rapidly learn and implement recent advances
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foundations of deep learning, functional analysis and measure theory. You can find more information about our research area and our team on the website: http://www.olgamula.com. As a team, we understand the
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). Team player and great collaborator Strong interest in interdisciplinary work at the interface between dementia/ neurodegeneration, modeling, and machine learningPrior experience in deep learning, or/and
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moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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, computer science, bioinformatics, or other related disciplines is required. Strong interest, research background and experience in the methodology research in statistical genomics, machine/deep learning
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groups to study cancer biology using deep clinicogenomics data and cutting edge NGS diagnostics. The Opportunity: Opportunity to drive scientific, analytic, and technical innovation in a community of
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in one or more of the following areas: computational biology/bioinformatics, biomedical informatics, deep learning/machine learning, data mining, biostatistics, or a closely related area. Proficiency
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Apply now Job no:533435 Work type:Auxiliary Location:Northridge Categories:Full Time Duties and Responsibilities: Join an inspiring convergence research team that champions deep integration across