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Field
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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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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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but not mandatory). A strong interest in materials simulation, quantum materials, and condensed matter physics. Experience with Python. Fluency in English is required. Finnish language is not required
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within a Unix/Linux HPC or cloud computing environment Proficiency in programming (e.g. using Linux/Python and R), command-line applications, and programming standards such as version control High level
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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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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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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
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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 and
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and phylogenetic comparative analyses using R or Python Present research findings at scientific meetings and symposia Prepare and contribute to the publication of results in peer-reviewed journals Your
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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