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vision, XR and generative models, specifically for capturing challenging scenarios and training deep learning systems to create better experiences for human users and learners. You will contribute
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 23 days ago
will focus on the following main tasks: 1) Machine-learn optimal reaction coordinates for the barnase-barstar complex, starting from ~0.1 millisecond high-dimensional MD (generated by a previous PhD
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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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an increased interest in adapting and developing the latest machine learning methods for the purpose of malware detection, and preliminary results are encouraging. The specific goals of this project include
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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of Finance and Economics beginning August 2026. The department seeks an individual with the credentials to teach undergraduate courses in Microeconomics or Macroeconomics, in person or online. A Master’s
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of Mathematics is specifically interested in candidates who are passionate about teaching and mentoring students at the undergraduate and graduate levels. We expect candidates to teach some of our undergraduate
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processing, or networking (preferred). You have strong background in machine learning or deep learning (experience with deep learning frameworks such PyTorch and TensorFlow is a plus). You have solid
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. Experience in artificial intelligence or machine learning approaches applied to materials research will be considered a plus. We regret to inform that only shortlisted candidates will be notified. Hiring
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast