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automation. 14. Ability/willingness to supervise PhD students and work as part of a larger team. 15. Optical/laser lab experience. 16. Experience with machine learning platforms such as TensorFlow, scikit
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Qualifications 1. A PhD (or close to submission) in Bioengineering, Optics, Medical Physics, Machine Learning or closely related fields. Experience 2. Demonstrate ability to work well as part of a team and
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machine learning classification of fluvial systems to generate a time-series of sediment erosion and deposition from optical satellite imagery. These data will be an essential component of the wider project
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contributions to our software base – integration of different solver blocks, profiling and tuning, GPU porting, … - and through this acquire knowledge and skills around the simulation of physical challenges
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practice. The position will provide the opportunity to develop one’s career by, for example, enhancing leadership and management skills, learning about and implementing new research methods, leading and
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provide the opportunity to develop one’s career by, for example, enhancing leadership and management skills, learning about and implementing new research methods, leading and contributing to research
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. Candidates with strong track records in computational tropical geometry, non-archimedean analysis and geometry, machine learning, and related areas are encouraged to apply. Hybrid and flex working arrangements
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of a catalogue of material composition of satellites 2. Developing material degradation models and estimating the material properties based on life and degradation. 3. Developing machine learning