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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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science approach and aims to use Machine Learning and Social Network Analysis methodology to 1. analyze the current and developing opinions of new clean energy technology among the Curaçao population and
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allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high
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experience-driven lifelong learning. Our world-renowned experiential approach empowers our students, faculty, alumni, and partners to create impact far beyond the confines of discipline, degree, and campus
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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targets to treat anhedonia. Proposals that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects
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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
. Proficiency in at least two of the following programming languages: Python, R. Experience in Machine Learning and Computational RNA Biology are desirable. Hands-on experience or understanding (the limitations
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
attention to detail and quality; strong computer skills, including Microsoft Office suite (Outlook, Word, Excel, and PowerPoint); Internet proficiency; and knowledge of or ability to learn human resource
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Hagenberg (50%). Job Duties: Independent research in symbolic artificial intelligence, computer algebra, computational logic, or automated reasoning with connection to machine learning Participation in
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• Trainees (postdocs and students): 45 • Research spans eight hubs—Statistical Learning & Data Science; Genomics & Genetics; Imaging; Mobile & Wearable Data Science; Epidemiology; Health Policy; Medical