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exciting opportunities for machine learning to address outstanding biological questions. The PhD student to be recruited will be working on the development of machine learning methods for single-cell data
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computational framework, integrated with deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit
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for managing smart cities. The team has gained substantial experience in machine learning for road traffic monitoring. They are now keen to thoroughly explore the additional opportunities presented by
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profile PhD (awarded within the last 5 years) of high quality, ideally in machine learning (in the broad sense), complemented by a strong mathematical foundation in probability and/or statistics Research
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The candidate should preferably have a PhD in Computer Science or Robotics with a solid background on deep learning and 3D scene understanding. Experience with LiDAR and Computer Vision is a plus. The candidate
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an advantage. The position is for three years, with an ideal start date between May 1 and September 1, 2025. The PhD project is focused on investigating spatial vision, appearance and redundancy
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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, computational material design, machine learning, big data, data analysis, material science, interdisciplinary topics Desirable (i) Experience in working with machine learning tools in the context of materials
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doctorale et l'établissement en ce qui concerne le taux d'encadrement de doctorants par un directeur de thèse. Le laboratoire IRIMAS propose ce sujet : HYBRID METAHEURISTICS AND MACHINE LEARNING METHODS
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Inria, the French national research institute for the digital sciences | Montpellier, Languedoc Roussillon | France | 3 months ago
11 Mar 2025 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile Recognised Researcher (R2