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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with
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such as scalable identification algorithms, uncertainty quantification, and the integration of learning-based models with formal verification. We offer a supportive, inclusive, and collaborative research
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on building dynamic system models for both the energy conversion technologies and the greenhouse climate, integrating these into a unified framework suitable for state estimation, predictive control, and
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datasets (e.g. ground-based radar measurements and weather station data) Contribute to the development and application of numerical models Assess uncertainties for future sea-level projections Publish
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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—influence emotional activation, cognitive information processing, and subsequent economic behavior. The empirical part of the work will be based on a controlled laboratory experiment in which auditory stimuli
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crosslink highly diverse bioeconomic production systems and to control them in a knowledge-based, adaptive and largely automated manner. We conduct research in dialogue with society, policymakers, industry
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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that control synaptic transmission and plasticity at various levels and using a variety of techniques and analysis tools that we developed. Where to apply Website https://www.academictransfer.com/en/jobs/359921
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adaptability of robotic systems. In particular, the research will investigate distributed and/or game-theoretic control frameworks for multi-robot systems, integrating optimization-based planning with learning