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prototype different thermal technologies and evaluate as well their integration and control within given hybrid systems, and under different market scenarios and industrial applications. Among
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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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empirical support in research for the view that temporal information, in combination with diversification, plays a crucial role in controlling risk. The project aims to investigate how abrupt changes in
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School of Engineering Sciences at KTH Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics at KTH and Science for Life Laboratory is a research group funded by
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measurement and control platform for optimal island operation of Chalmers’ wind-battery system. Machine learning-based forecasting tools for renewable production using limited local measurement data
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maximal signal-to-noise and detection of picomolar levels of protein in the blood. The laboratory’s part of the project is to test and optimize this new strategy and subsequently use it to evaluate
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technology, or a related field. You have a strong background in hydrodynamics and ship manoeuvring. You should also have a good understanding of control theory and optimization. Additional requirements
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properties. In this project, we will apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and
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while optimizing sequence properties. An emerging frontier focuses on designing proteins that act as templates for inorganic interfaces, forming symmetric oligomers that control inorganic material
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, the focus will be on implementing inverse design, optimization, and/or machine learning for designing and optimizing devices in integrated photonics, which will be subsequently fabricated and tested by our