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-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning frameworks that integrate
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 18-Dec-25 Location: MD Type: Full-time Categories: Academic/Faculty Computer/Information Sciences Internal Number
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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Position Title: Postdoc Research Associate - Materials Science Appointment Type: Post Doc/Trainee Job Description: Summary of Duties and Responsibilities: The candidate will conduct cutting-edge
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proposals. Responsibilities Develop, implement, and evaluate new statistical and machine learning methods aligned with the two themes above. Lead and co-author manuscripts in statistical, machine learning
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 14 hours ago
for this position. The Post-Doc chosen for this project should have a broad range of skills including at least several of the following: Development of experimental methodologies that allow the rigorous collection
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. Candidates are expected to have experience in one or more of the following areas, such as: wireless networks/communications, machine learning/vision, intelligent transportation systems, intelligent sensing
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(Word, Excel, PowerPoint, Outlook)and computer-based tools with the ability to develop and successfully utilize various analytic and reporting tools relative to physician utilization, clinical quality
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communication skills orally and in writing are mandatory. Who we are All post doc fellows will be part of Center for Ice-Free Arctic Research (CIFAR). You can find more about the center here: https://bio.au.dk
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 18-Dec-25 Location: MD Type: Full-time Categories: Academic/Faculty Computer/Information Sciences Internal Number