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DOE L-level security clearance Qualifications We Desire: Demonstrated experience with model-based reinforcement learning for real-time control applications Demonstrated experience with convolutional
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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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, PCR, sequencing, gene expression analysis). • Skills in experimental entomology and infection models (rearing and maintenance of mosquito colonies under controlled conditions). • Ability to design and
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | about 16 hours ago
the Collaborative Research Center SFB 1324, which is a highly collaborative network of labs investigating the mechanisms of Wnt signaling in animal development and disease. Wnt signaling controls tissue patterning
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computing infrastructure. The main tasks will include: designing, implementing and documenting programming components supporting experiments with AI models (including LLM, generative models, complex system
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and measurement noise. • Developing predictive control strategies based on reinforcement learning or hybrid approaches compatible with real-time adaptive optics constraints. • Proposing and
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of Systems and Control, Department of Electrical Engineering, focused on innovative research in Data-driven Modelling and Optimisation for Fast Charging of Lithium-Ion Batteries. About us At the department
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will focus on the development of advanced thermoelectric cooler designs for on-chip cooling applications within the MGICIAN Doctoral Network. The project will address model-based and data-driven design
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focuses on the study of large-scale circulation in the Atlantic Ocean, combining oceanographic data analysis, satellite observations, and numerical modelling to better understand tropical–subtropical