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Field
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models; Learning with limited supervision; Generalisation beyond pretraining distributions The precise research agenda will be developed together with the successful candidate and may evolve throughout
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, and will have the following specific responsibilities: • Manual annotation of videos (L3 level with bounding boxes) of deep-sea biodiversity to train artificial intelligence (AI) algorithms and feed a
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the relevant effects and factors are difficult to quantify and are inherently uncertain, whilst decision-making power is distributed across several stakeholders, asset levels, and time horizons. The coordination
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techniques and algorithms (e.g., clustering, decision-tree learning, artificial neural networks). Experience scripting and programming in several languages with common data science toolkits. Proficient use
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learning algorithm that leverages information contained in low-amplitude gravity perturbations (coined PEGS for Prompt Elasto-Gravity Signals, Vallée et al., 2017) to provide rapid unsaturated magnitude
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criteria The candidate must have some experience and a strong knowledge of artificial intelligence (AI) applications of the following fields: nonlinear analysis (gradient flows of probability distributions
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, supported by adaptive scheduling and execution algorithms for distributed quantum applications, and validated in both simulated and real-world environments. Secondment periods are planned at the project
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. We develop novel optimization methods, Machine Learning algorithms, and prototypical Energy Management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids
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scheduling, resource allocation, and workload placement algorithms for distributed AI training; Improve the communication efficiency, scalability, and reliability of decentralised training frameworks; Evaluate
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Infrastructure? No Offer Description Mission: Conduct research in the field of quantum computing and its modular and distributed architectures based on cryogenic interconnections and leading-edge error correction