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experiments; supporting other group members with data analysis and interpretation from both simulations and experimental data; and use the developed framework to design new materials with optimised performance
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This post is full-time, hybrid and fixed term for 12 months The Grantham Research Institute on Climate Change and the Environment (GRI), based at the London School of Economics and Political Science (LSE), is
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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are seeking candidates with a PhD (or near completion) in a relevant field, or with substantial and demonstrable research skills (academic or professional) that reflect equivalent capability, to contribute
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the decision analytic modelling field research field and contribute to high quality reports for funding bodies and peer-reviewed outputs. You will hold a DPhil/PhD in health economics or a related quantitative
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administrative activities, with guidance as required, ensuring that project aims and objectives are met. Working closely with the supervisory team, develop and plan research objectives. Present information
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the decision analytic modelling field research field and contribute to high quality reports for funding bodies and peer-reviewed outputs. You will hold a DPhil/PhD in health economics or a related quantitative
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analysis. Candidates are also expected to bring their own knowledge and approaches to design and execution of the project. The candidate will work in a dynamic team of scientists and collaborate with group
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progression to PhD depending on performance during the initial period. Main duties and responsibilities Admin - ordering consumables, tracking cost and use of consumables PIL training - opportunity to acquire a
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technologies. Key Duties and Responsibilities Design and conduct scientific and engineering experiments on plant-based materials for medical manufacturing. Evaluate biocompatibility, mechanical robustness