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. MINDnet aims at addressing the challenge through a holistic optimization - from individual computing devices to the overall architecture, including a focus on applications, and training methods - across
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early detection is a need that needs to be addressed using advanced sensors. The candidate will apply machine learning and IA methods to anticipate the evolution of the discharges. This project aims
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collaborate with ARCHIVES project partners to ensure coordinated progress and sharing of results. · Develop solutions combining numerical modeling, mathematical methods, and statistical/AI approaches
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14 Jan 2026 Job Information Organisation/Company Academic Europe Research Field Medical sciences » Other Medical sciences » Health sciences Educational sciences » Teaching methods Researcher Profile
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Centre Euopéen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS) | Toulouse, Midi Pyrenees | France | 14 days ago
Calcul Scientifique) is a private research organization developing advanced methods for the numerical simulation and the algorithmic solution of large scientific and technological problems of interest
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methods for the analysis of large-scale genomic, clinical, and phenotypic data, including phenome-wide association studies (PheWAS), statistical genetics, and precision medicine applications. This includes
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systems, including methods for colloid characterization Spatially resolved surface analysis using interference microscopy and autoradiography Derivation and parameterization of mechanisms Interdisciplinary
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and good knowledge of opensource development practices; • solid understanding in at least one of the following areas: numerical methods, computational modeling, machine learning or continuum mechanics
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Offer Description This project investigates vibration-based methods for monitoring the structural health of deep underground tunnels throughout their lifetime. Structural Health Monitoring (SHM) based
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candidate with a strong background in some aspect of numerical analysis for PDEs and an interest in scientific machine learning and probabilistic methods, who enjoys working in collaborative inter