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with a strong academic background in Machine learning, Natural Language Processing, and/or Software Development. Applicants with an experience in one or more of the above academic fields are encouraged
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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MRIs (available in HIE cases) and radiomic features predictive of school-age MRI extracted. Machine learning algorithms of radiomic features predictive of future brain development will be developed
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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. Observational experience/expertise to compare computational results with data from (sub)mm observations, especially ALMA, are highly appreciated. Expertise in applying machine-learning techniques is an additional
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Python and R; - Demonstrable experience with Machine Learning; - Excellent problem-solving skills and the ability to work both independently and as part of a team. This position is for full-time, on-site
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. Additional optional skills and qualifications: Experience in computer vision for intelligent processing of documents. Contracting requirements: Presentation of the academic qualifications and/or diplomas