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. Our research integrates expertise from machine learning, optimization, control theory, and network science, spanning diverse application domains such as energy systems, biomedical systems, material
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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Advanced Grant to determine the optimal combination of epitopes that elicits the highest level of protection. Within the research group, we value a positive work environment built on respect and
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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to ensure optimal tissue concentrations during surgery. The PhD student will utilise national and international arthroplasty registry data, adapt in vitro diagnostic tools such as the Minimum Biofilm
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found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production and in the interaction between these areas. The research covers
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-fabrication processes for superconducting devices Automatic bring-up and calibration of quantum processors Design and simulation of quantum processors Optimal-control techniques for high-fidelity qubit
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above Qualifications Appointment to a position as a researcher requires that the applicant has a PhD. Required qualifications: a PhD within within the field of microscale acoustofluidics postdoctoral
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natural science or engineering. Documented knowledge in environmental science and life cycle assessment (LCA), food science, modelling and simulation, agricultural production chains and industrial ecology