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transfer and laser-matter interactions. The “design-manufacture-inspect-model-test” approach of this project will equip the successful PhD candidate with a wide range of valuable and transferable skills
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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this fundamental challenge, the PhD candidate will be part of a wider team to establish methodological framework, combing utilisation of controlled tree growth test, thermodynamic modelling and advanced optical
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of Vienna. This currently includes one full professor, one tenure-track professor, four postdoctoral researchers, four PhD students, an organisational assistant, and student assistants and interns (see https
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7: C9 A comprehensive and up-to-date knowledge of current issues and future directions within the wider subject area or subject specialism. C10 Knowledge of project specific technical models
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the optimization-based methods (doi.org/10.1016/j.apenergy.2020.116152 ), 3- Weakness of the model-predictive-control (MPC) against HESS’s parameters uncertainties, noises, and disturbances (doi.org/10.2514/6.2022
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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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approaches to interpreting these large datasets, as well as computational models that capture low-dimensional structure that reflects the architecture of the neocortex. By working with researchers developing
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production-grade system that integrates Vision Transformers for visual deepfakes, advanced Natural Language Processing (NLP) models for phishing detection, and a dedicated Explainable AI (XAI) layer
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reduction (MAR) algorithms, AI-based segmentation, and automated 3D anatomical modelling, promise clearer, more reliable imaging. Integrated effectively into clinical workflows, these advances have the