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effects on semiconductor components and all aspects of irradiation testing. You should have experience with SEE rate prediction tools and sufficient knowledge of the space environment to correctly employ
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effects on astronauts. Detailed analyses of such effects, and possible ways to shield and mitigate against them, are typically conducted by using environmental prediction in combination with Monte Carlo
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AI-based decision-making, learning approaches, and predictive capabilities to enable spacecraft to react proactively to events while reducing reliance on ground intervention. A key focus will be
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. what has happened) as well as predictive control (i.e. what is likely to happen), and supporting the implementation of potential solutions. Contribute to exploratory projects involving machine learning
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data fusion, signal decomposition/unmixing, incremental/few-shot learning, and conformal prediction. Academic or professional proficiency in Python and its core scientific/ML frameworks: TensorFlow, JAX
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-enabled mission planning, immersive XR training, predictive maintenance, and digital continuity. The environment is highly collaborative, combining ESA experts, industry partners, and research institutions
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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern
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of enterprise integration (APls, middleware, SAP Pl/PO and BTP) Familiarity with low-code/no-code solutions, for example Power Apps and SAP BTP Familiarity with Microsoft applications, such as SharePoint and .Net
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of existing studies to promote the use of risk-informed decision frameworks, prediction models, AI applied to planetary protection. Tasks include: Support the creation of probabilistic models for planetary
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required. Knowledge in coding languages, geotechnical engineering, and materials processing is an asset. Diversity, Equity and Inclusiveness ESA is an equal opportunity employer, committed to achieving