81 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Harvard University
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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protocols, which involves conducting literature reviews and eliciting required data elements Understanding of and experience with quantitative methods (simulation modeling, optimization, and machine learning
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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Bioinformatics / big data , Biological Data , data science , Interdisciplinary Cellular and Molecular Biology / Cellular and Molecular Biology , RNA Biology Computer Engineering / Cloud Computing
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. Cross-Disciplinary Fellowships (CDF) are for applicants with a Ph.D. from outside the life sciences (e.g. in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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organization skills Interest in learning and strengthening existing skillsets Strong technical and programming skills, familiarity with current deep learning techniques, with a focus on computer vision Special
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training activities focused on biomedical research methods in healthcare. Under the close supervision and mentorship of Dr. Zitnik, the Associate will: 1) Explore and learn about state-of-the-art techniques
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research assistants (RAs); Machine learning skills; Writing papers for management and economics journals; Interest in reskilling initiatives; Working with partner organizations or companies. Basic
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network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering Candidates should have demonstrated publication records in cardiac and vascular engineering or