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research threads in Computer Vision and Machine Learning : Improving and creating state-of-the-art foundation models to be able to enhance both performance and computational efficiency; Design world models
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Science, or related field Knowledge of flexible antennas, wireless communications, and machine learning Good skill set in signal processing and optimization techniques Proficiency in Python for modeling and machine
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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analysis Large language models or machine learning/predictive modeling for longitudinal data analysis Strong computer programming skills Strong mathematical or statistical skills Ability to work as a part of
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 23 days ago
leverage machine learning techniques to bypass IO bottlenecks in the context of physics simulation on high-performance computing (HPC) clusters. This work is thus placed in a broader ``Machine Learning for
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testing and optimisation of various decarbonisation scenarios. For more information please visit the TransiT website https://transit.ac.uk/. We are seeking a proposal for a project contributing to Digital
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: · A completed M.Sc. degree in computer science, machine learning, and related fields. · Strong proficiency in English (the working language of the institute). · Capability and willingness
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Biology Scientist in Single-cell omics & AI to support the valorization trajectory of a computational platform combining single‑cell omics, AI machine learning, and translational biology. The role involves
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety