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computational cluster and/or high-performance computer with Linux OS. Excellent oral and written communication skills and a collaborative working mindset. Strong publication record in closely related research
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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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of an undergraduate degree in a related field. Training in causal inference and/or machine learning methods Effective oral and written communication skills Outstanding academic credentials and intellectual creativity
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detailing the status of ongoing work, using spreadsheets, word processing, and graphics computer programs, participating in lab meetings, and accomplishing general laboratory tasks and other related tasks as
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related to battery materials, correlated electron calculations, including via DFT+U, supercells, dynamical mean field theory or experience in defect and/or alloy calculations, machine learning, and other
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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members. Learn more at https://hr.duke.edu/benefits/ Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, gender
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integration Key areas: § Metagenomic and 16S rRNA sequencing analysis § Single-cell RNA-seq and proteomic/metabolomic data integration § Machine learning and AI applications in microbiome data
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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are essential. Preferred Qualifications: Prior experience with machine learning (ML) in high-energy physics is highly desirable, though not required. Appointment Details: The position is expected to be based