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Computational Cost by Machine Learning and DFT-Based Data, Journal of Chemical Theory and Computation, 2024, 20 (16), 7287–7299. Funding category: Contrat doctoral PHD Country: France Where to apply Website https
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Washington, D.C. Connections working at Johns Hopkins University More Jobs from This Employer https://main.hercjobs.org/jobs/22103428/modeler-x2f-data-scientist-i-x28-epidemiology-x29 Return to Search Results
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Programme? Not funded by a EU programme Reference Number 231 / 2026 / 1878 Is the Job related to staff position within a Research Infrastructure? No Offer Description Your Responsibilities Participation in
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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models—including myocardial infarction, tachypacing-induced heart failure, and atrial fibrillation—are essential for understanding disease mechanisms and therapeutic targets. The University of Manchester’s
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collaboration with diverse communities, researchers, clinicians, and policy experts. You will work alongside a multidisciplinary team and use both qualitative, quantitative and computational modelling methods
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Adaptation) models to support a significant research portfolio that contributes to the goals of the TransiT , DARe and ACES projects. The team at Heriot-Watt maintains a suite of computational logistics and
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Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description