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start date Programming skills in Python, R or STATA Willingness to learn unfamiliar tools as needed (e.g. GitHub, APIs, new libraries). Familiarity with econometric techniques, especially panel data
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affected and shaped narrative traditions would be an advantage. The LCRSW is driven by a singular, transformative vision: to learn from the past in order fundamentally to change how the world understands
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, machine-learned interatomic potentials, molecular dynamics, kinetic Monte Carlo modelling and comparison with experimental data from the Faraday Institution FAST programme. Faraday Institution PhD students
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-learning tools. This project will prepare you for starting a career in nuclear decommissioning or applying emerging technological and modelling approaches to facilitate circular economy innovation in
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chemistry. Working with us means learning something new every day. You will be affiliated with the research project, with the working title Q4-BIO, where we open a new field of research around quantum tools
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characterisation equipment. This largely experimental PhD will provide transferable materials characterisation skills, including optical and scanning electron microscopy. The successful candidate will also learn
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this industrial PhD studentship in Physics – fully funded by the University of Exeter and Leonardo UK. We’re looking for a student who has a passion for science, with ambition to learn and apply their own ideas
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closed‑loop carbon platform suitable for large‑scale deployment. Person Specification Motivation, creativity, and resourcefulness A mature approach to learning Candidates should have been awarded
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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. Coding and hardware skills are desirable. Strong analytical/mathematical skills. Passion about research and willingness to learn. Good presentation, communication and writing skills. Funding support After