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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional
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postdoctoral researcher, you will use various techniques to investigate fiber-reinforced structures in nature (i.e., fungi), including additive manufacturing, computer simulations, materials testing, and
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to the ground segment and operations domains, for the procurement and delivery of data systems in support of ESA’s Space Safety Programme, and as a matrix support provider to ESA programmes and mission operations
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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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programme in 2008. Through the CCI, ESA is developing a suite of global data records of key components of the climate system, known as essential climate variables (ECVs). The climate-quality datasets produced
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for this position, the following is required: PhD in systems engineering, computer science or informatics, and the subject of the thesis should be relevant to the task description provided above (e.g. digital twin
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); units such as onboard computers, mass memories, remote terminals and instrument control units*; digital and analogue signal processing electronics for payload/platform functions; front-end acquisition and
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learning with a background in robust and embedded control, aerospace systems and physical modelling, as well as in artificial intelligence-related concepts, computer vision and numerical optimisation
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. The Directorate of Space Transportation is a programme directorate within the ESA matrix structure, whose activities are performed in cooperation with the other ESA directorates and the Director General’s Services
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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation