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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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for age estimation, as well as the development of new, robust methods. To this end: Approaches to be implemented and compared: classical point regression, probabilistic regression, and quantile regression
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. Desirable Criteria Experience implementing machine learning or deep learning models (e.g., neural networks, probabilistic learning methods). Knowledge of state estimation techniques, such as Kalman filters
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-view learning that are robust under distribution shift and missing modalities. The key objectives include: Develop scalable Bayesian deep learning methods for uncertainty estimation in modern neural
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focused on immune repertoire profiling. Building on the foundational TIRTL seq (Pogorelyy and Kirk et al., Nature Methods 2025) method, we provide cohort scale T cell and B cell receptor (TCR and BCR
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estimation method for the hazard function within the framework of cure models. This method aims to provide flexible, data-adaptive estimators with optimal statistical properties (optimal convergence rates and
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Course: PhD studentship Project title: Exploring a One Welfare approach to human-animal-computer interaction in digital dairy technologies Principal supervisor: Charlotte Doidge Other supervisors
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AI methods and systems, and support the University’s mission in advancing high-impact research, innovation, and global research excellence in AI and computing. Job Responsibilities: Expected to work
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, Microsoft PowerPoint, Microsoft Outlook Required Other Computer Applications: Required Additional Knowledge, Skills and Abilities: 1) Knowledge of the principles and methods of Indigenous education 2
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projects as well as third cycle courses, seminars and conferences. You will perform standard physics-based methods to estimate absolute and relative binding free energy. Based on these, you will develop