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: genetics, epigenetics, inflammation, metabolic pathology, autoinflammatory pathology, autoimmunity, arthritis, computational analysis, mathematical modeling, applied algorithms, machine learning in biology
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skills and experience: Essential criteria PhD or equivalent (or thesis submitted*) in at least one of the following subjects: Computer Science, Machine Learning, Biomedical Engineering, Medical Imaging
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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team. Essential duties: *Data cleaning, data preprocessing, and data manipulation of large datasets *Development, training, and evaluation of machine learning and deep learning models *Feature
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 7 days ago
creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search, interpretation
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
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quantitative focus on these fields Solid foundation in statistics and/or machine learning, e.g., supervised learning, regression modeling, model evaluation, or high-dimensional data analysis Good programming
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the Research Promotion Foundation, RIF, (EXCELLENCE/0524/0337), Title: “Machine Learning for Intelligent Insect Monitoring” and proposes an automated early warning system that will be able to detect and classify