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12 Dec 2025 Job Information Organisation/Company University of Amsterdam (UvA) Research Field Computer science Mathematics » Algebra Mathematics » Algorithms Mathematics » Discrete mathematics
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cutting-edge analytical approaches (Multilevel Vector Autoregressive Models, Dynamic Structural Equation Modelling, Hidden Markov Models, Causal discovery algorithms, Reinforcement Learning), Contributing
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of this project for a one-year period (100% full-time commitment) to make a significant contribution to the implementation of machine learning (ML) algorithms. The postdoc is expected to have proven experience in
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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences
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algorithms Predicting structured output Self-supervised learning Computational metabolomics Computational biomedicine Computational drug discovery Excellent technical and communications skills are required
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conducted implementations of algorithms and simulations using contemporary GPU hardware, or Profound knowledge and experience of the DUNE software environment (https://dune-project.org/ ) (knowledge and
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, Atmospheric and Oceanic Sciences, Geosciences, Computational Science and Engineering, or a related area is required.The position will involve developing models and algorithms for the evolution of inorganic
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward