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Field
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understanding of challenges of building large-scale systems. Programming skills in Python. A good Bachelor’s Hons degree (2.1 or above or international equivalent) and/or Master’s degree in a relevant subject
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external enrolment procedures. Selection criteria Demonstrated experience in programming and system development. Expertise in Python programming and data analysis. Experience developing Machine Learning
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take on administrative tasks in research, teaching and administration. Your profile: Completed Master's / Diploma degree in the field of Astrophysics Extensive programming knowledge (Python) Excellent
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languages for data analysis such as Python, C++ and Matlab. Knowledge with various microscopy instrumentation such as dual beam focused ion beam/scanning electron microscopy (FIB/SEM) Behavioural Competencies
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interest in historical research, migration, and digital infrastructures Familiarity with digital tools and languages such as Wikibase, SPARQL, RDF, Python, GitHub, and Jupyter Notebooks is highly desirable
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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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into account as disruptive events, and which strategies can be derived from this for a resilient system design. Your contribution to scientific analysis: Further develop existing energy system models in Python
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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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deep learning, generative models, or biomedical imaging/omics data Strong programming skills (Python, PyTorch, etc.) A commitment to interdisciplinary collaboration and impactful research How to Apply
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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