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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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-driven biocatalysis and accelerate bioprocess development. DC1: Machine learning-guided multiparametric optimisation of cytochrome P450 monooxygenase PhD enrolment: Technical University of Denmark DC2
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analysis, and basic feature engineering. Experience with Python or a similar programming language, and basic exposure to scientific computing or machine learning libraries, combined with an interest in
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support
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flexibility orchestration Scalable data and machine learning pipelines Digital twin architectures for cyber-physical energy systems AI-based energy system modeling, simulation, and optimization Secure and
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close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective