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Field
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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algorithms, representation and learning of data and dynamical systems, geometric deep learning, topological and algebraic data analysis, optimization on manifolds, and operator- and PDE-based approaches
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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learning approaches (Roux) to evaluate novel biomarkers for ADRD. Minimum Qualifications PhD in Computer Science, Engineering, Bioinformatics, Biomedical Data Science, or a related field Experience in
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: Design and implement ML, deep learning, and Large Language Models for orthopaedic applications Work with multimodal clinical data, including: Medical imaging (X-ray, CT, MRI) Electronic health records (HER
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CW and pulsed laser systems, spectrometers, high-resolution cameras, and delicate optical components are desirable Expertise in advanced data analysis techniques (Machine learning and Deep learning
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» Health law Economics » Health economics Engineering » Biomedical engineering Researcher Profile Established Researcher (R3) Positions PhD Positions Application Deadline 23 Aug 2026 - 23:59 (Europe/Brussels
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climate change pest research / collaborate in open, inclusive biophysical ecology group / deep computational work with the Julia programming language Apply now to shape predictive models that guide pest
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of a PhD in Food Science, Food Chemistry, Food Engineering, or a closely related discipline An established research profile with national recognition and a strong record of publications in high-quality