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: • Knowledge of fluid dynamics, especially experimental methods in atomisation and sprays. • Programming experience (e.g. Python, MATLAB or similar). • Experience with data analysis, machine learning
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include cloud based Trusted Research Environments (TREs.), Microsoft Azure, SQL, Python and Git. You will be based within the Faculty of Health and Life Sciences, reporting to the CHIL CTO, and will work
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to and build upon modern Python-based statistical modeling tools such as pyhf, supporting likelihood publication, large-scale analysis combinations, and result reinterpretation across the ATLAS
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proficiency in programming languages (e.g., Python, C/C++), knowledge of control algorithms and/or existing architectures (e.g., ArduSub, ROS2) for autonomous navigation, and the ability to develop innovative
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in support of research hypotheses. Implement and document code in Python (preferred), including data manipulation and estimation routines. Produce tables, figures, and written summaries of results
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data analysis workflows in Python Conduct citation analysis of innovation and collaboration patterns Assist with machine learning and/or LLM-based analysis Participate in mentoring junior researchers in
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include cloud based Trusted Research Environments (TREs.), Microsoft Azure, SQL, Python and Git. You will be based within the Faculty of Health and Life Sciences, reporting to the CHIL CTO, and will work
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instrumentation, measurement systems, and data acquisition Good skills in signal processing and image processing Proficiency in at least one scientific programming environment (Python or MATLAB) Basic knowledge
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technology. Documented experience in data-driven urban energy modeling with Python and machine learning as well as experience in administrative tasks is also an advantage. Terms of employment The employment is
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processing and image representation Demonstratable experience in python for data handling and algorithm development Desirable : Knowledge and experience with imaging systems (X-ray CT, MRI or similar