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using Python, ASE and relevant DFT software analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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leverage quantum acceleration where beneficial, while maintaining practical deployability and system reliability. Required knowledge The candidate should have: Strong programming skills (Python
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leverage quantum acceleration where beneficial, while maintaining practical deployability and system reliability. Required knowledge The candidate should have: Strong programming skills (Python
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interfaces, reaction mechanisms, activity and selectivity develop reproducible atomistic simulation and high-throughput workflows using Python, ASE and relevant DFT software analyse electrochemical
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computer programming background who has an interest in ecology and biodiversity conservation, or an ecologist with computational modelling experience (e.g., using R, Python, Matlab). Project funding
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computing Strong programming and data-analysis capability, preferably including Python and C/C++ A record of high-quality research outputs commensurate with opportunity and demonstrated ability to
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing
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diffraction and advanced diffraction methods. Density functional theory calculations. Python programming, or a willingness to learn. Higher degree student supervision. Strong initiative, resourcefulness and
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, computer science, bioinformatics or a closely related discipline strong programming skills in R or Python experience developing or applying methods to complex or large-scale datasets ability to work