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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
to the use of low-parametric models such as SMPL. Using many videos for redundancy can allow to acquire more detail, but at the expense of computation speed. And all this detail needs to be animatable, which
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geological modelling and/or geophysical imaging Familiarity with site investigation, borehole logging, and geophysical approaches Experience with machine learning or deep learning is preferred Good written and
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on provenance and integrity of machine learning models and pipelines. You will tackle topics related to: Verifiable, provable and auditable machine learning (e.g. adversarial robustness, privacy, fairness). AI
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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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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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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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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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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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. 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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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