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Field
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privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within
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strong foundations in chemical or bioprocess engineering and hands-on experience in computational modelling. You will lead the development of the bioprocess physics layer of the digital twin and support
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that occurs within these biological neural networks, so that these networks can be leveraged for AI applications. In addition, you will develop mathematical and computational neuroscience models
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Artificial Intelligence (AI) models are widely used in decision making procedures in many real-world applications across important areas such as finance, healthcare, education, and safety critical
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation", IEEE Transactions on Dependable and Secure Computing 2019 #digitalhealth
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on high-performance computing systems, and assessing model predictive skill. The outputs will inform national policy guidelines to support regulatory frameworks and enable ecologically sustainable
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healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series
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. This project aims to enable the widespread adoption of such tools by: AimsCreating a pre-trained model that can detect rodent body part locations in videos of common behavioural tests, without requiring manual
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conducting research experiments, including therapeutic treatment of in vitro and in vivo models of pancreatic cancer as well as a 3D human explant model of the disease, assessment of therapeutic response and