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planning, Green Infrastructure (GI), environmental modelling, spatial analytics, digital twins for planning and urban sustainability. The project addresses critical challenges in planning for nature and
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or machine learning methods to tackle predictive questions. Proficiency in building and validating statistical methods and/or machine learning techniques in R or Python are also essential. Applicants
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skills (Python preferred) and solid understanding of machine learning and deep learning, including computer vision techniques. Ability to read, write, and communicate scientific texts clearly; strong
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. Criteria Essential or desirable Stage(s) assessed at Experience in developing systems using a variety of technologies. Python followed by Java are our current stacks. Essential Application/ interview / task
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outcomes for nature. You will have strong ecological fieldwork experience, GIS skills and project planning expertise. You will benefit from being a member of a world leading research group in a School
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Python Excel (VBA) AWS ANSYS Experience in deployment and use of IoT devices Essential Application/interview Experience of successful team working, including teams made up of internal colleagues and
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assessment (e.g. panel data analysis, multiple regression, AI-enabled forecasting, geo-spatial technique, python and advanced coding skills). Examples of tools include Brightway, Open LCA, SCEnAT, FPSCRS
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immunohistochemistry, microscopy and live cell imaging. Essential Application Experience in image analysis or bioinformatics analysis. Essential Application Knowledge of programming languages, e.g. Python/R, and the
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. Analyse, categorise and document collected seismological, geological, tectonic and built environment data and model and map the data in GIS environment. Develop high-fidelity Finite Element (FE) models of
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statistical methods, and their application to healthcare technologies research. Essential Application Excellent programming skills in Python, Julia or similar, including scientific computing and code curation