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for systematic reviews, Mendeley for citation management and SPSS for data/statistical analysis/machine learning. Diversity, Equity and Inclusiveness ESA is an equal opportunity employer, committed to achieving
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(UQ) for machine learning and its validation. Your areas of research will be chosen based on both your own expert judgement and insight into trends and developments and on team requirements to ensure
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records from satellite data, and/or improved methods of uncertainty characterisation, including the use of artificial intelligence and machine learning to improve or analyse satellite climate data records
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required. Experience with the design, development and verification of TT&C and PDT subsystems for space applications is required. Very good knowledge of modern computer systems, simulation and modelling
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testing. Expertise with analogue electronics design, computer-aided design (CAD) or electromagnetic simulation is an asset, as is experience of working on projects and in large teams. Knowledge and
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very good knowledge of on-board digital signal processing techniques and technologies for RF payloads and microwave instruments will be considered an asset. Very good knowledge of modern computer systems
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performance in accordance with the respective service level and application of internal processes. This includes contributing to risk management definition, mitigation actions and lessons learned exercises
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for a four-year assignment. During this time, you will be actively working and learning on the job and will benefit from valuable mobility and developmental opportunities that will prepare you for a
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technologies, for example, using machine learning techniques to support long term exploration; Topics related to ‘off world living’, e.g. human factors, design and concept illustration; Crew Health and
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technologies, for example, using machine learning techniques to support long term exploration; Topics related to ‘off world living’, e.g. human factors, design and concept illustration; Crew Health and