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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
at the Centre Inria de l'Université de Lille in the Scool team. He or she will be in contact with experts of sequential decision making. The candidate will study different research questions related
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About the Role The position is funded through the EPSRC project “Zeros, Algorithms, and Correlation for graph polynomials”. We study various combinatorially defined polynomials such as the
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data and IT infrastructure. Combination of different algorithms to test multimodal predictive modelling. Compilation and presentation of data orally at seminars and conferences, as well as independent
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algorithms to shape the liveable cities of tomorrow? Job description Human-centred AI techniques, such as Reinforcement Learning from Human Feedback (RLHF), hold great potential for supporting design
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of electron microscopy, tomography, or related modalities. You have demonstrated experience in developing algorithms or computational methods, for example in image simulation, optimization, or data analysis
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to research on language and speech development across different populations and contexts, including children acquiring language in different cultural settings outside of the United States to children with
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software development work and running complex robotic experiments with different platforms. Job requirements Required qualifications and qualities: MSc degree in software engineering or robotics, and similar
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to make a difference. If this sounds like you, you’ve come to the right place! Responsibilities: Conduct original research on joint communications and radar sensing for spaceborne systems, with a focus on
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, modulation classification, sensing, and adaptive spectrum optimization in diverse operational environments. Your work will focus on modeling and algorithmic aspects related to the development of highly
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operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer