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insights into actionable strategies for pediatric care. Our work combines statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data
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. The research project focuses on the design, development and validation of Artificial Intelligence and Machine/Deep Learning models applied to healthcare, with particular reference to computer vision applied
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discrete choice modelling, behavioural data science or machine learning? Are you interested in developing the next generation of AI tools that accelerate scientific discovery while maintaining behavioural
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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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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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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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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 8 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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cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively