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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
learning, privacy, security and distributed systems. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-10411 Requirements Skills/Qualifications We are looking for a candidate with
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remaining biologically interpretable? The PhD candidate will design and apply integrative computational workflows using methods such as multi-omics integration, spatial modelling, representation learning
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interest in data analysis, modelling, statistics, and machine learning. Experience in spatial data analysis (GIS), scientific programming (Python, R, or equivalent), or artificial intelligence will be
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models
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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
DFKI, with a specific focus on the data, alignment, and representation-learning foundations required for robust and generalizable sign-to-text translation. Motivation and context Sign languages
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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of technological disruption driven by Artificial Intelligence, we propose to analyze the data and quantify these similarities by exploring various applications of machine learning methods. With the advancement of AI