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configuration of physics-based simulation environments (e.g., different simulators and robot models) Modeling and implementation of contact-rich handling and assembly operations in simulation Exploration
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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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well as societal applications in cancer treatment, radiation-tolerant microelectronics, and other radiation-based technologies. Duties The doctoral project will investigate the competition between different reaction
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central scientific challenge will be to learn integrated representations of forest ecosystems from datasets with very different characteristics, resolutions, coverage, and levels of supervision
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engineering, robotics or a comparable field Basic knowledge of Python, ideally with PyTorch Familiar with ROS2 Interest in AI agent architectures and human-machine interaction Enjoy programming and are eager
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between structure and electrochemical characteristics of different types of carbon materials. The researcher should, thus, be well-versed in both theoretical and experimental electrochemistry and have
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. The programmes are essentially similar to ESA’s own Graduate Trainee Programme, notably in offering the same employment conditions. They however differ in one important respect: each NGT Programme is funded by
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such as Python, C++, and MATLAB, scientific computing libraries, geospatial/HDF5 data formats, APIs, containerized environments, automated testing frameworks, and version-controlled collaborative
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from pediatric patients. These data arrive from different countries, sequencing platforms, and clinical metadata formats, and are subject to data-transfer agreements and data-protection requirements
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Competences Programming competence in R and Python Independent troubleshooting / problem solving Rigor and attention to details Effective communication in meetings Ability to document your code, pipelines