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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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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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python-based automations; and integrating GIS data with other software platforms and business processes. Working in close collaboration with interdisciplinary FBO managers and external partners, this role
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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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, GIS/GeoAI, UAV applications, and resilient infrastructure planning. The candidate with experience translating cutting-edge research (e.g., AI) into operational decision-support systems for agencies and
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. • Strong proficiency in Python and relevant libraries for data analysis and modelling (e.g., TensorFlow, Keras, Scikit-learn, Pandas); knowledge of R would be an asset. • Familiarity with geospatial data
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projects-tracking misinformation, analyzing climate change data, and building machine learning models-before advancing into GIS (Geographic Information Systems), network analysis, and responsible technology
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sustainability, environmental systems, and spatial data Skills in the following areas: Data analysis and programming, e.g. Python, R GIS and spatial analysis Data visualisation Language Requirements: Applicants
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librarians and staff , including those involved in GIS, Robotics, Makerspace Labs, the Teaching and Learning department and specialized subject librarians. The ideal candidates will demonstrate strong
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programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication skills, and excellent English. Relevant