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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine their antibiotic resistance. Your work will
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for molecular dynamics (MD), slashing computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 11 days ago
neoclassical tearing modes in metal-wall scenarios; support the creation and validation of machine learning (ML) and hybrid physics + ML models to monitor and control the proximity to stability boundaries
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biology-experimental and/or theoretical biophysics-experimental and/or computational genomics-computer science, statistics, and/or machine learning with applications relevant to genomics-bioinformatics
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-disciplinary research at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction. Subject area The subject area for this position is Computer Science. Background
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical
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pandemic risk policy and practice. The PDRA will drive a research project within the centre focussed on the application of machine learning and bioinformatics approaches to the prediction of pandemic risks
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natural language processing and machine learning workflows; (3) experimental design and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal