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Field
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open to candidates with a strong interest in either: i) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane
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| Bioacoustics | Precision Livestock Farming | Artificial Intelligence | Machine Learning | Deep Learning | Animal Behaviour | Acoustic Biomarkers | Sustainable Agriculture | Digital Farming View All Vacancies
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modeling; pharmacogenomics; health informatics; data science, machine learning, or deep learning; causal inference methods Strong written and oral communication skills, with at least one first-author
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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expected to learn basic R or python to generate plots and analyze data. Qualifications Requirements for employment are: You have deep knowledge of immunology including autoimmunity You have experience
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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations