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Chemistry to conduct fundamental and applied research at the intersection of machine learning and computational chemistry. In this role, you will develop physics-informed, symmetry-aware models
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their post-co-op reflection. Integrated Learning : Cooperative education is an integral part of the academic experience at Northeastern and our philosophy is that learning in the classroom is enriched by
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computational neuroimaging initiative focused on individual-level brain characterization. It combines large-scale imaging data, normative modelling, harmonization and machine learning. The platform generates
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, working closely with the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. You will develop and evaluate natural language processing and machine-learning methods (including large language
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California State University, San Bernardino | San Bernardino, California | United States | about 9 hours ago
: - Participation in relevant coursework or projects. - Experience with data analysis or related internships. - Exposure to machine learning or AI projects (academic or personal). Special Conditions: Compensation and
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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the Defense Innovation Network Assistant (DINA) initiative. The position conducts applied and translational research in machine learning, natural language processing (NLP), retrieval-augmented generation (RAG
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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thinking around the development of school culture Model instructional coaching Collaborate on monitoring systems Facilitate thinking around professional learning supports to staff Facilitate problem solving
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vision, multimodal learning, medical/industrial imaging, and related AI4X topics. Design, implement, train, and evaluate deep learning models and research prototypes using Python/PyTorch and related tools