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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery
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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data management and version control (Git/GitHub
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-based cameras (e.g. DVXplorer), and tactile/force sensors. 3. Strong background in computer vision and deep learning, with practical implementation experience. 4. Proficiency in programming with
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statistical or machine learning methods to complex datasets. Evidence of involvement in grant writing or development of independent research ideas. A commitment to teaching the next generation of researchers
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R or equivalent skills in another relevant language. We are not expecting you to be an expert in all forms of computer simulation, Large Language Models, or machine learning etc, but a working
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of atomistic modelling of ferroelectric materials 2. Experience in development and application of machine learned potentials * Please note that this is a PhD level role but candidates who have submitted