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degree in Information Systems, Computer Science or related field plus at least 1 year of relevant experience to include: Experience in Python Developing web applications using modern open-source JavaScript
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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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of extracted experimental parameters Design and execute Python-based scripts to infer derived parameters and standardize units and formats Integrate external databases and online resources to enrich curated
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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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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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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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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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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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available datasets. Utilize advanced statistical and analytical tools (Python, R, SQL) to derive actionable insights from data. Lab activity support Participate actively in team meetings, brainstorming