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the Master in Architecture under the supervision of Prof. Dr. Florian Hertweck.
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, logs, traces) to monitor platform health and guide performance improvements. Technical Architecture & Advisory: Advise research teams on designing cloud native, scalable architectures aligned with
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loss functions Study calibration and post-calibration for predictive uncertainty Integrate uncertainty modules into MLFF architectures Detecting Extrapolation and Low-Reference Regimes Analyze MLFF
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force fields (MLFFs) that combine state-of-the-art equivariant neural network architectures with robust, well-calibrated uncertainty estimates. These models will enable fully automated active learning in
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translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model–graph neural network architectures for gene perturbation prediction, including the design and
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to the selection and analysis of technology baselines and system architectures for spaceborne JCAS payloads, including trade-offs under realistic platform constraints Implement and integrate J-CROSS building blocks
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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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communication protocols (e.g. Modbus, IEC 61850, EtherCAT, OPC UA, CAN/CANopen). Deploying cloud-to-edge architectures for real-time operation of IBR-dominated grids, integrating edge controllers with cloud
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inversion methods (LUT and hybrid approaches) Profound knowledge in machine learning and deep learning methods for remote sensing applications, including architectures such as CNNs, LSTMs, and Transformers
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knowledge in machine learning and deep learning methods for remote sensing applications, including architectures such as CNNs, LSTMs, and Transformers, and deep generative models (e.g., VAEs, normalizing