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cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively
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discipline Demonstrable research experience developing machine-learning methods for robotics, control or autonomous systems, including reinforcement learning, imitation learning, model-based learning
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modeling of complex, multi-site datasets. In this role, you will bridge the gap between complex data infrastructure, cutting-edge machine learning, and synthetic data generation—building automated
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, telecommunications, or a related field (with a CGPA of at least 4/5) Experience (e.g. coursework, projects, etc.) in AI and machine learning, including understanding of various DNN architectures, training models, and
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Principal investigators or co-principal investigators as a Jr. Data Researcher for a wide range of projects and activities involving machine learning (ML) and artificial intelligence (AI), including autonomy
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 14 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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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations
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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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developing machine-learning methods for robotics, control or autonomous systems, including reinforcement learning, imitation learning, model-based learning or an equivalent approach. Experience designing 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