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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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questions into robust machine learning implementations, including preprocessing, model training, validation and deployment is part of your role; collaborating enthusiastically with domain specialists and
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Site (UMS), a unique test environment for utility mapping technologies. Current machine learning models and their training data are limited in size, comprehensiveness, and realism – resulting in partial
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19 Sep 2026 Job Information Organisation/Company Radboud University Research Field Computer science » Modelling tools Computer science » Programming Engineering » Computer engineering Engineering
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in high-tech, medical, and energy systems. Information Modern engineering increasingly relies on data-driven models to describe complex dynamical systems. Scientific machine learning is now enabling a
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machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a biological reference point for identifying disease-specific biomarkers. A central part of the
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
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structure modeling in cancer immunotherapy design. Profile A — AI PhD in machine learning, computer science, computational science, or a related field. Strong experience with deep learning (e.g., PyTorch
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systems and control theory, circuit theory, optimization, and machine learning, with the ultimate goal of advancing the mathematical foundations of physics-based learning. Your responsibilities include