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will apply machine learning — in particular physics-constrained symbolic regression — to discover compact analytical spin-Hamiltonians and their parameter dependencies. These Hamiltonians will feed large
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project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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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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researchers to design and develop a wide range of innovative projects, for example involving causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via
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. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine