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machine learning for safe and optimal control of cyber-physical systems. The projects are expected to be funded by the VILLUM INVESTIGATOR project S4OS (“Scalable analysis and synthesis of safe, secure and
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, we will investigate how traditional computer-science techniques for verification and falsification as well as machine learning can be tailored to these goals. This way, the project lays the grounds
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machine learning is advantageous Interest in data mining and database management Programming skills e.g. in R or Python Good analytical and communication skills. Proficiency in written and spoken English is
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the interplay between qualitative and quantitative methods and data. There is a growing focus on novel computational methods such as NLP, machine learning, and AI within the group. Teaching activities in
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threats, and safe NLP models, contributing to a safe and secure society. Using insights from Cybersecurity to improve systematic security in NLP models. The candidate should have an MSc. in Machine Learning
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, MATLAB, and/or C++/C#) Knowledge of machine learning techniques, particularly for time-series data Background in prosthetics or human-machine interfaces is advantageous PhD Stipend 2: Adaptive Control
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internet Quantum embeddings for machine learning Networked quantum sensing supported by distributed classical communication Prospective applicants to this PhD proposal should have the following
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transformers, Machine Learning, Power systems. Expertise with the following engineering tools and programming languages can be an advantage but not limited to: MATLAB, PLECS, PSCAD, others
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converging operational research, machine learning, and decision-making methodologies. The ultimate goal is to create real-time autonomous systems that are not only trustworthy but also adaptive in the face of
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similar and have substantial experience with machine learning. Experience with generative AI for image is a large advantage, and computer vision experience is a plus. Furthermore, as AI:Xpertise is about