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reactions, along with constitutive models for glassy and semi-crystalline biodegradable polymers, building on recent works from the Brassart group. You will implement the models in a finite element software
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to understand and predict how technologies evolve — from artificial intelligence to net-zero innovations in energy, transport, and carbon capture. By building a global database on technological progress, we seek
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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following areas: qualitative thematic analysis; mixed methods research, ethnographic methodologies. Evidence of research-related computing skills: word processing, NVivo, Reference management software
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out rigorous and impactful research into the computational mechanisms of human learning using deep neural network models, and disseminating the findings within the research group, across the wider
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statistical knowledge and expertise in statistical analysis (including multivariate analysis and data management) as well experience in relevant software packages, e.g., SPSS/STATA and R, you will have
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for biomedical data will be advantageous. The successful candidate will contribute to high-impact publications, open-source software development, and training materials. This position is full-time and fixed-term
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—and machine learning to work within established research programmes. A strong, demonstrable experience in research software development using Python programming language and modern machine learning
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to understand innate reactive metabolite signalling pathways of importance in well-defined disease states. The project is funded by ERC Advanced Grant. Find out more about the Aye research and group at: About you
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of comparative genomics and molecular evolution analyses to identify these targets. The project will make use of software developed in the Kelly group as well as other widely used software for biological sequence