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using machine learning methodologies; (2) the extension of an existing CFD framework for multiphase modeling to the case of PEC systems; (3) the implementation within the framework of a description of
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architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en
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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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highly collaborative research team to accelerate and extend ongoing research efforts (including DL-based joint species distribution modeling and causal machine learning) and lead methods development
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of machine learning technologies, including large language models within the Department of Food and BioResource Technology, with a special focus on technologies applicable in so-called developing countries. As
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | about 1 month ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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Models to push the frontier where computer vision, physics simulation, and embodied AI converge. Join Us! This position is part of a collaborative research programme between the University of Amsterdam
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records, aiming to co-create practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in machine learning, neuroscience