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. optimization and machine learning techniques) to prepare ports, terminals, shipping companies, and other port actors for this important challenge. Your research will be part of the PortCall.Zero project - a five
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description Positive tipping points
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/scientific fields; Programming experience in languages such as Python or C++; Knowledge of Nvidia Omniverse; Familiarity with AI or machine learning techniques applied to simulation, control, or optimisation
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wind speed and sea ice extent. The baseline algorithms designed to estimate such variables from the observed data at Level 1 (L1) are based on Machine Learning (with the exception of that for Freeze/Thaw
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Applications. The candidate will be embedded in the Massivizing Computer Systems (MCS) group, which focuses on research in distributed computing systems and ecosystems, and currently spans over 40 diverse people
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical
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) Topic 4: On-board Digital Signal Processing and Machine Learning/Artificial Intelligence The objective of this topic is to model and implement functions commonly used in on-board processing, assessing
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qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS
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online learning platform with 100+ different courses; 7 weeks of birth leave (partner leave) with 100% salary; partly paid parental leave; the possibility of setting up a workplace at home; a pension at
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative