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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining
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on developing algorithms to analyse optical signals extracted from video images to obtain vital signs with high accuracy. Key responsibilities include: Robust feature extraction from biomedical signals
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This research focuses on developing and evaluating methodologies for the optimal design of control charts within the framework of Statistical Process Control (SPC). The study aims to determine the
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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these challenges, calling for the development of responsible AI systems that are transparent, trustworthy, and aligned with human values in educational contexts. This PhD project aims to design, develop, and
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-world datasets from different databases & platforms (AWS, SQL, Azure) Using energy systems data analysis and modelling and the development of open-source tools e.g. in Python, R, Matlab or Excel Creating
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. This project aims to develop VR/AR applications that allow users to explore protein structures interactively and immersively, enhancing comprehension of protein function, behavior, and their roles in food
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produce software implementations of the algorithms developed in this project. About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and
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these issues is critical for building trustworthy multimodal AI systems. Research Objectives The goal of this PhD project is to develop scalable Bayesian uncertainty estimation frameworks for single- and multi
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practice of imaging, cardiac mapping and targeted radiation therapy Create, develop and experimentally and clinically implement mechanistic and AI based algorithms Support the acquisition and analysis