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Field
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vast amounts of short, irregular biomedical time-series data, including physiological signals, lab results, and interventions. These data hold great potential for supporting high-stakes, complex decision
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exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis, natural
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electron microscopy analysis, Raman spectroscopy, fluid inclusion analysis, potentially appropriate petrochronological methods, and 3D geological modelling. The project will be conducted in partnership with
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on the following criteria: Knowledge in electric power engineering, power electronics, and power system analysis Experience in modelling, simulation, and experimental work Proficiency in Swedish and English, both
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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community engagement. In this role, you will: Contribute to research on ecological and spatial data for urban environments: Conducting fieldwork, data collection and analysis related to urban air quality and
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, working closely with data scientists, oncologists, and international collaborators. The research will inform real-world decisions in oncology and contribute to tools that improve patient care. We’re looking
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. You will examine, for example, how security of supply can be realistically mapped in a sector-coupled energy system, which political decisions, crises, or technological developments need to be taken
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the trusting; and the situations where we are more likely to ‘outsource’ moral decisions to AI agents. By understanding this, we consider the ethical promise and pitfalls of such moral machines and how
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of the Discrete Choice Experiments in Mozambique and South Africa, and the analysis of the ensuing data Develop computational models of health actors’ decisions and responses to regulatory options Collaborate with