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trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By
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data on homeowner retrofit needs and preferences. Undertaking research trials to test and refine the AI algorithms used in our platform. Meaningful assistance in research and policy development with a
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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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to leverage on the fast-developing expertise in multi-disciplinary forest research in Luxembourg, LIST, in collaboration with the University of Luxembourg and the Luxembourg Institute of Socio-Economic
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– depending on the successful candidate’s background and interests. Your tasks: Develop new exact and approximation algorithms and perform complexity analyses for optimization problems on (temporal) graphs
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simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power consumption and
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(https://metaproteomics.org/ ) formed a strategic alliance to set up a powerful and interdisciplinary network called METAMIC to capitalize on the potential of metaproteomics for developing novel strategies
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will join an international and multidisciplinary team and will work in the project which aims to develop an innovative, modular, and partially bioresorbable wound filler for Negative Pressure Wound
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on the theoretical and algorithmic development of control methods that combine physical modeling and real-time computation. The work will involve deriving reduced-order models, designing controllers that exploit
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nature. The PhD candidate will focus on the theoretical and algorithmic development of control methods that combine physical modeling and real-time computation. The work will involve deriving reduced-order