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high-quality experimental datasets using laboratory flume facility. Developing and validating hydrodynamic models to simulate the effect of vegetation in different environmental conditions. Designing and
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the University of Edinburgh. We can offer opportunities for you to develop in your career and make a real difference in the communities around us while contributing to the world at large. The University
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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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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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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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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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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
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& writing) in English Competences • Programming competence in R and Python • Independent troubleshooting / problem solving • Rigor and attention to details • Effective communication in meetings • Ability
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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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laboratory hardware. You are interested in instrument automation and quantitative data analysis. Experience with Python, LabVIEW, or Matlab for instrument control/data processing is required, but you do not