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placement locations. Advanced metaheuristic optimisation algorithms, implemented in Python and interfaced with DIgSILENT PowerFactory, will be developed to determine the most effective PST locations and
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experience of data science analytical packages and R or Python would be advantageous. Funding The project is funded by the UKHSA and Nottingham University Business School. As a UK public body, UKHSA can only
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of AI/ML will be an added advantage. Proficiency with optimization tools and software like GAMS, CPLEX Proficiency with programming languages preferably Python, Matlab Fluency in communication and
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comfortable applying analytical thinking to explore research questions and have programming experience (preferably in Python). You have an affinity with coastal numerical modelling, e.g. AeoLiS, XBeach
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into longitudinal studies and in using functional MRI and scripting-based languages (e.g. Matlab, R, Python). They must also have experience of conducting research with human participants and of performing highly
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analyse large, multidimensional 4D STEM datasets. Develop or adapt software tools (e.g. Python, MATLAB) for image reconstruction, phase mapping, and quantitative analysis of ferroelectric domain wall
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experience with programming tools such as R or Python. An interest in acquiring new data science and programming skills is required. A keen interest in teaching and academic development. An excellent academic
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include: Developing the model using open-source Python software Planning and conducting experiments Analysing teardown reports and experimental data Validating and improving the model Publishing results in
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settings, with 5+ years of experience building impactful deep learning systems A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR Strong programming skills in Python and/or C
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demonstrate experience in or interest in working with R/Python. You can demonstrate very good writing skills. You can demonstrate a proactive attitude. You have good communication skills, and a collaborative