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: 10 May 2026 Apply now Predicting ecosystem dynamics under global change requires accurate, high-resolution soil information. Existing global soil maps are largely based on empirical machine learning
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superconducting magnets and play a key role in connecting research, technology development, and international collaboration. You are a source of information on your area of expertise and work closely with engineers
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requirements You should have: the ability to summarise complex information; knowledge of the handling of restricted or classified information; the ability to work effectively in an international team environment
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, community-driven research software. Both institutions provide a full-time position each for the duration of 1 year, and both candidates will closely collaborate with each other and with data researchers in
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, to be separated from the gravity signal in the data processing, measured by accelerometers on each satellite. MAGIC will be composed of two pairs of satellites. The first pair, P1 (GRACE-C), is being
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droplet impact performance in the laboratory. - Implement droplet size dependent and impact velocity dependent cumulative damage models in a digital twin based on meteorological data for site
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of competence. Technical competencies General background and specific experience in the technical domains covered by the position Experience with optical design, optical performance and generic data analysis and
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, ensuring that all appropriate information is available to all Project participants, such that they can discharge their responsibilities efficiently within the agreed framework. You will also be responsible
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Previous Next Sign up Cookies We use cookies and similar technologies to process your personal data (e.g. IP address) for personalised content, ads and third-party media integrations. Functional and strictly
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the design, implementation, integration and validation of prototype and pre-operational services making use of EO data, AI/ML techniques and modelling environments relevant to SRN applications; contributing