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Field
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, machine learning applications, and analyses of individual disease trajectories. Your tasks will include: Integrate multimodal neuroimaging datasets from MRI, PET, EEG, and fNIRS studies Characterize brain
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, and physics-informed machine learning open new possibilities for establishing grid foundation models. However, it is still unclear which model structures is most capable and suitable, how well they
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optical materials, enabling applications like tunable linear dichroism. Such optical responses can be controlled via external stimuli, including electric/magnetic fields, light, temperature, or chemical
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Your Job In this position, you will be an active part of our AI Consulting Team. Together with our partners, we develop new and innovative applications of Machine Learning. You will connect to
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on mass spectrometry or electric current detection. Despite their high sensitivity, these methods remain limited to specific polymers and a low level of parallelization. Our project aims to explore a
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will investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable
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surrogate models and experimental data from a pilot-scale settler are available. The project offers close supervision from doctoral researchers in machine learning and process control. Your Profile
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overcome this limit. They can simulate the response of materials to an electric field with near-DFT accuracy on systems of up to a million atoms. Using our in-house machine-learning framework, you will
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in terms of detection accuracy and reliability Your Profile Ongoing masters degree studies in computer science, data science, engineering, machine-learning, or other closely related field Good
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feeds Post-doctorat - Méthodes de machine learning pour la simulation rapide des ondes de choc 3D - H/F 172 / 753 vacancy Détail de l'offre Description du poste Domaine Sciences pour l'ingénieur