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data using recommended guidelines and machine learning tools Defining the uncertainty sources Enhancing existing guidelines for full-scale power-speed assessment practice Disseminating research findings
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. Kinetic rates will be calculated on the fly from molecular dynamics simulations using machine learning potentials. This approach will provide guidelines to steer the formation process of zeolites by tuning
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numerical methods, as well as familiarity with concepts in complex systems, physical memories or machine learning. We strongly believe in the benefits of an inclusive and diverse research environment, and
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leveraging visual perception of human actions, object properties, and scene context. It integrates machine learning tools, knowledge representation, and robot actions design to develop decision-making
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spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication
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. To this end, the candidate is expected to have a good knowledge of programming tools and acquire knowledge about our custom systems during the initial stage of the doctoral studies. Responsibilities and
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degree in Computer Science, Artificial Intelligence, Data Science, or related field; Solid background in machine learning, deep learning and foundation models such as Large Language Models; Strong
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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: www.uni.lu/snt-en/research-groups/finatrax/ The candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with specialisation in Information Systems. In the context of Prof
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quantified, and machine learning will be used. Duties As a PhD student, you will work toward a doctoral degree as the final goal, according to the goals specified in the Higher Education Ordinance. In parallel