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in physics-informed machine learning, surrogate modelling, generative AI, or design optimisation would be desirable. An entrepreneurial mindset and a willingness to build commercial acumen alongside
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Job Title: Associate or Senior Editor, Nature Communications (Machine Learning) Locations: Shanghai, Beijing, Nanjing, Pune or New Delhi (Hybrid) Application deadline: 1st November 2026 - Candidates
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at NTU are looking for a Research Fellow (RF) to carry out in research in probabilistic machine learning, causal discovery and GenAI, by exploring cutting-edge approaches such as causal representation
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acoustic monitoring, automated species identification and machine learning, transform the way we monitor wildlife populations and detect ecological change? What could your PhD involve? There is no single
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George Washington University | Washington, District of Columbia | United States | about 10 hours ago
and statistical machine learning, optimization, and algorithmic decision-making. We value both methodological advances and rigorous applied research on AI that help organizations improve prediction
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innovations. The Pediatric Disease Modeling Lab (https://dbe.unibas.ch/en/research/data-driven-modelling-analysis/pediatric-disease-modeling-lab/) is seeking a Data and Computing Technician to build and
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unsupervised learning. These methods will be based on new classes of partial differential equations on graphs and new graph neural network models based on diffusion processes. The developed algorithms will be
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. The role combines hands-on experimental mechanics with rigorous simulation and data analysis and benefits from complementary expertise in scientific machine learning and constitutive modeling of anisotropic
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model (LLM) APIs to generate simulated respondents across factorial conditions. Career Readiness Competencies Set personal goals and track progress toward them. Adjust tone and approach based on audience
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UNIVERSITE DE BOURGOGNE (University of Burgundy) | Le Creusot, Bourgogne | France | about 17 hours ago
active sensor in the control loop of complex robotic systems. This team conducts research on robust control, state estimation by vision, visual servoing and machine learning applied to cyber-physical