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; Interest in interdisciplinary environmental assessment and offshore renewable energy systems; Experience with programming and/or data analysis (e.g. Python, R, MATLAB, GIS); Excellent written and spoken
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supports the development of critical thinking, data analysis, problem-solving, and project management skills while providing opportunities to publish in leading journals and present at international
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bioaccessibility · Scientific support in the preparation, implementation and evaluation of dietary intervention studies · Statistical analysis, interpretation and presentation of scientific data · Publication
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is offering two full-time PhD studentships funded by the Engineering & Physical Sciences Research Council (EPSRC) to start in the 2026-27 academic year (either September or January entry is possible
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levels. A central component of your work will be the collection and analysis of qualitative empirical data. The empirical work will be based on in-depth case studies of firms operating within the Danish
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into cavities and waveguides for applications to optical quantum information. In this context, the PhD project will consist of optical characterization and quantum optics experiments based on single-photon
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
The Faculty of Engineering, Department Applied Mechanics, Research Group Thermodynamics and Fluid Mechanics is looking for a PhD-student with a doctoral grant. More concretely your work package, for
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be an advantage: Programming and data analysis experience Experience with working on high-performance computers What we offer: Research freedom: Develop your own ideas within the project’s broad scope
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, or other quantitative data analysis, and some experience with statistical software packages; you are keen to develop these skills further. You possess strong and documented scientific writing skills. You
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, resulting in inconsistencies across soil properties and underperformance in data-scarce regions. This PhD project will develop next-generation machine learning methods for geospatial prediction by integrating