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theoretical challenges motivated by astrodynamics, optimisation, control, scientific machine learning, mission design and autonomous systems, translating these into new research directions in deep learning
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by candidates within five years of receiving their PhD. In particular for this position, the following is required: PhD in data science, AI, computer science, machine learning, aerospace engineering
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topics such as quantum-enabled navigation and timing, artificial intelligence-/machin learning-enabled system enhancements, advanced and fully digital payloads, reconfigurable architectures, resilient PNT
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schemas across heterogeneous datasets. Familiarity with data exploitation approaches, including scientific data analysis environments, machine learning readiness and user-oriented data services. Experience
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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in general, demonstrated expertise in analysis and proven practical experience in testing. Expertise with analogue electronics design, computer-aided design (CAD) or electromagnetic simulation would be
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supervision, with excellent communication and presentation skills. Technical and computational knowledge, including in machine learning, applied to climate science would be an asset. You should be prepared