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within macrophages – key cells of the innate immune system. The Hill Group uses Salmonella enterica serovar Typhimurium as a model pathogen to investigate how host–pathogen interactions contribute
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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modelling to study the causes and consequences of extreme chromosomal instability in these cancers. The role will involve: - Learning and applying cytogenetic methods for generation and analysis of chromosome
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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be on simulating the printing process, which requires, e.g., the definition of a proper material model that adequately describes rheological aspects and the adjustment of extrusion-related parameters
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help develop and characterise advanced patient-derived tumour models and use them to test promising therapeutic targets that exploit vulnerabilities caused by loss of the SMARCB1 gene. This role offers
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of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, general pre-trained transformers, prompt engineering, knowledge graphs, knowledge
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Science, Robotics, AI, or a related field Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human-centred AI
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Science, Robotics, AI, or a related field 2. Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human
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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and