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Field
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collaboratively Meritorious qualifications: Experience with machine learning or deep learning Experience with computer vision or image analysis Experience with 3D data, geometric representations, or parametric
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Science or equivalent degree) in biological sciences or bioinformatics Experience with population genomic and population genetic research Deep understanding of Evolutionary Biology Experience or interest in
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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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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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ambitious Novo Nordisk Foundation Data Science Collaborative Programme, “Synthetic health data: ethical development and deployment via deep learning approaches (SE3D)” which is a collaboration between Head of
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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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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