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learning, optimization algorithms, and interoperability frameworks for optimal energy management across Europe. KTH leads technological landscape analysis, multi-energy investment planning tool development
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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viruses and individual cells to evolutionary biology and global biodiversity. Taking on research studies at the Department of Biology generally means focusing on a delimited part of the research area of
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, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation group. Main responsibilities Conduct research in collaboration with senior researchers and
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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with .root files and explore different event generators as well as machine learning tools and algorithms. The nature of LDMX as an international project will require you to work collaboratively in
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significant role in learning in AI by enabling cognitive agents to acquire actively knowledge and skills through interaction with their surrounding environment. Embodied AI requires tools, algorithms, and
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both academic research and industrial applications. In addition to theoretical research, the work might involve implementing new algorithms in the SCT tool Supremica, which is developed by the Automation
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, within the subject of the position, e.g. within genetics, genomics, evolutionary biology using bioinformatical approaches or within molecular biology addressing evolutionary questions. While there are no