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technology model that analyses integrated satellite and ground-based sensor data; estimating European launcher carbon footprint, energy security and energy cost savings; proposing meaningful carbon footprint
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-target areas (such as water, soil, operator, bystander and residents), and to optimize systems to produce food in harmony with nature. The research New spray application methods aim to reduce plant
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in-depth experience in one or more specific technical domains covered by the position Experience with the design, development and application of relevant tools and methods Understanding of related
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assessment. You will be provided with access to various engineering and computation toolsets along with the high-performance computer. A good background in numerical methods and computational platforms is
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); apply and refine evapotranspiration estimation methods based on these in-situ observations (e.g. Makkink, Penman-Monteith, SPAC-models); develop spatial upscaling techniques using remote-sensing data and
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: autonomous and fault-tolerant systems (including health monitoring systems), advanced guidance, control, estimation and optimisation techniques and tools, and the technology development of GNC sensors with a
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for aerospace. Experience in managing and monitoring industrial activities Experience with the application of relevant methods and software tools. Behavioural competencies Education A master's degree in physics
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Qualities:You are a computer scientist, a computational biologist or a computational chemist with proven expertise in implementing machine learning methods in Python. You write or have written your master's
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the position Hands-on hardware experience Experience in the development and verification of space hardware Experience with the design, development and application of relevant tools and methods Understanding
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Experience with the design, development and application of relevant tools and methods Understanding of related technologies, R&D trends and the industrial landscape Project support experience in a relevant