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nanoagents leads to a global heating until a steady state is reached. However, this macroscopic point-of-view ignores the nanoscale spatial heterogeneity of the temperature distribution while strong thermal
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15 Mar 2026 Job Information Organisation/Company CNRS Department Institut de Science des Matériaux de Mulhouse Research Field Chemistry Physics » Chemical physics Physics » Biophysics Researcher
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, particularly in the context of signal estimation through filtering (Figure 1). This problem is particularly relevant for large-scale systems (e.g., energy distribution networks, sensor networks, gene regulation
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28 Mar 2026 Job Information Organisation/Company CNRS Department Institut Pierre-Simon Laplace Research Field Environmental science Environmental science » Earth science Environmental science
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chemical production, the focus is on optimizing yield, distribution, or conversion by testing different operating conditions (pressure, concentration, temperature, etc.) and at isothermal conditions. Then
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, Interactive and Cognitive Systems, Distributed Systems, Parallel Computing, and Networks. The host team, DAISY, is a joint CNRS, Grenoble INP, and UGA research team handling research challenges
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11 Mar 2026 Job Information Organisation/Company IC2MP Department Fundamental and applied sciences Research Field Chemistry » Analytical chemistry Environmental science » Natural resources
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23 Feb 2026 Job Information Organisation/Company Centrale Lille Institut Department North Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions
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PhD student at ILL on Advanced Neutron Imaging for Defect Mapping in Repaired Aero-Engine Components
6 Mar 2026 Job Information Organisation/Company European Synchrotron Radiation Facility Research Field Physics Researcher Profile Established Researcher (R3) Positions PhD Positions Application
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
, in particular diffusion models that can capture complex data distributions. These models are further extended to sample efficiently from the full Bayesian posterior, which is usually computationally