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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
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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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on deep learning coupled with molecular simulations for the investigation of slow variables and transition pathways describing large conformational changes and reactive processes in complex biological
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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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. Depending on the candidate's profile and interests, the thesis may develop along one or several of the following directions, at the crossroads of statistical physics, biophysics, and machine learning
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on lncRNA structure. These experimental constraints will then be used to guide deep learning-assisted RNA 3D structure prediction tools, in order to generate ensembles of structural models. Clustering and
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learning aspects, in order to connect the physical architecture, measurement protocols and classification performance. • Electrical and radio-frequency characterization of chains of magnetic tunnel junctions
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Lazuli collaborations. The recruited person will join the Cosmology team at IP2I and will work under the direct supervision of Dr. Mickael Rigault. Where to apply Website https://emploi.cnrs.fr/Offres/CDD
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that remain valid despite uncertainties in the drone and camera models. Incorporate air traffic rules and safety requirements into the mathematical optimization framework. - Optimization and Learning: Optimize