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logistics, and it is considered an advantage if you also have knowledge of, experience in, or a strong interest in sustainability issues. You show initiative, have a strong willingness to learn, and are able
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–liquid interfaces through an integrated approach combining atomistic (DFT, AIMD, MD), and continuum (FEM) Modelling, supported by machine learning frameworks. Key responsibilities: • Perform DFT/AIMD
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Predoctoral Researcher (PhD Candidate) in Computational Simulation and AI for Therapeutic Ultrasound
Madrid (Spain). • Ability to enrol in an official PhD programme in Spain. • Interest in numerical simulation, machine learning and clinical applications of focused ultrasound. • Availability for conference
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knowledge. This project requires specific and essential skills; however, these can be learnt throughout the PhD and with help from supervisory team. These may include: · Difficulties with learning how
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emergency responders. By combining global landslide data, innovative machine‑learning methods, and new ways of representing runout, the research will produce faster and more reliable nowcasts for use
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theory, Bayesian inference, Monte Carlo simulation, and statistical analysis of subjective data. Data science and machine learning - big data analytics, surrogate modelling, digital twin development, and
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in peer-reviewed journals Mentor junior research staff Teach one undergraduate or taught postgraduate course per year, if required; and Support and perform other duties as assigned Requirements
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with teaching and learning. Precision Medical Sciences UG degree: List A List B Medical Cell Biology Basic Medical Genetics Medical Chemistry Introduction to Medical Ethics Academic skills Academic
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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· Develop and apply transformer-based foundation models and machine learning methods for large-scale epigenetic datasets · Integrate longitudinal data and biological prior knowledge into AI models · Actively