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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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carbon below the surface to sustain roots and symbiotic mycorrhizal fungi, forming a dynamic exchange network that governs water and nutrient uptake. Despite its importance, this underground system remains
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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occurring below ground. Trees allocate a substantial proportion of their carbon below the surface to sustain roots and symbiotic mycorrhizal fungi, forming a dynamic exchange network that governs water and
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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Networks for MLFFs Implement and test uncertainty-aware loss functions Study calibration and post-calibration for predictive uncertainty Integrate uncertainty modules into MLFF architectures Detecting
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highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud
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Within the frame of the FNR-CORE funded project ENER-G (Empowering Networks of E-buses for Resilient and Green mobility), a doctoral research project is framed. The PhD topic is around and
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of the finite element method (modelling assumptions, boundary conditions, mesh/element choices, convergence checks). Experience with at least one FE tool such as Abaqus or similar. Scientific programming skills