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Field
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software (e.g. ArcGIS, QGIS) and coding environments (e.g. Python or R), collaborating across LUMHR themes, and supporting interdisciplinary research activity. Teaching support may be required, up to a
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scientific concepts to diverse audiences. Proficiency in computer programming (R, Python, or similar languages). This job posting reflects the general nature and level of work expected of the selected
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on research carried out within the ECUME GIS (e.g. Simon Police’s PhD thesis). To this end, working groups will be organised with GIS stakeholders and experts in maritime spatial planning, and realistic
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Twins & Energy Analytics Forecasting & Optimization Algorithms Green Hydrogen Technologies ICT Applications in Energy Systems GIS Applications in Energy Planning Interdisciplinary and innovative research
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teaching, learning, and research requirements. They are subject matter experts; they are skilled in research data management, collections, statistics, GIS, metadata, digital preservation, scholarly
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interest in Earth system and climate change sciences; hands-on and curiosity-driven, and ability to work independently and collaboratively; affinity with object-oriented coding (preferably Python), GIS and
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to interdisciplinary discussions Office, GIS (Arc/Q), R, Python (basics) English, Spanish (Italian, German, Dutch, Polish beneficial) Strong experience with spatial assessments. Early leadership experiences at junior
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(R, Python, etc.), and working knowledge of data management protocols. Experience with exposome or environmental data, multiomics integration, and exposure-wide association studies (ExWAS) and GIS
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quantitative discipline or equivalent experience. · Experience applying statistical or machine learning methods in real-world contexts. · Proficiency in Python and/or R for data analysis and modelling. · Strong
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: Strong interest in community engagement, data science, or social impact Experience with data analysis tools (e.g., Excel, R, Python, GIS, etc.) is a plus Excellent written and verbal communication skills