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Field
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity
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computing, domain-specific multi- and manycore architectures, networks-on-chip (NoCs), methods and algorithms for application parallelization, simulators and virtual platforms for application- and
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. Utilize AI and statistical techniques to extract insights from large time series datasets. Collaborate within an interdisciplinary team to develop innovative algorithms for forecasting, ranging from short
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, operation, and optimization of SPS- and PLC-based control systems for experimental equipment and beamline instrumention Integration of hardware and software interfaces (motors, detectors, sensors, sample
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the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure
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Implement and document numerical algorithms for calibration, synchronisation, and data transformation in a modular, traceable workflow Validate Level-1B outputs, including spectral and statistical quality
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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imaging with clinical text and decision support. Evaluate algorithms regarding robustness, explainability, and clinical impact in musculoskeletal medicine. Collaborate in an interdisciplinary team
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, based on existing Level-1 documentation provided by the GRACE-FO project Implement and document numerical algorithms for calibration, synchronisation, and data transformation in a modular, traceable