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We are looking to recruit a postdoc in the Electronic and Photonic Materials division in the Department of Physics, Chemistry and Biology (IFM) for research on the ultrafast optical spectroscopy
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optical systems to generate temperature gradients, as well as imaging solutions such as FLIM and Schlieren imaging, in close collaboration with experts in lasers and optics. Co-supervision of undergraduate
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Postdoctoral position in fabrication of hollow-core optical fibers for next-generation communication
technology, and electronics and systems engineering. The Department is now seeking a postdoctoral researcher to develop fabrication process for making hollow-core optical fibers for future optical
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making hollow-core optical fibers for future optical communication. The position is full time for two years with starting date September 1st, 2025 or according to agreement. Project description
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different mixing and reactive properties compared to conventional fuels. In this project, turbulent mixing and combustion of hydrogen in air will be studied through optical experiments and numerical modelling
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synthesis and self-assembly - Spectroscopy and quantum optics of collective photoluminescence - Scattering and diffraction studies of nanomaterials The specific scientific project will be refined in
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principles and compliance with data protection regulations (e.g. GDPR) Proficiency in containerization tools (e.g., Docker, Singularity) Experience developing reusable libraries or packages in R or Python
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principles and compliance with data protection regulations (e.g. GDPR) Proficiency in containerization tools (e.g., Docker, Singularity) Experience developing reusable libraries or packages in R or Python
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languages such as Python or C Solid background in quantum mechanics Experience from working on quantum error-correction, open quantum systems, quantum optics, bosonic codes or continuous-variable quantum
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. Machine learning: experience with algorithms such as nearest-neighbor, simplex projection, recurrent neural networks, singular value decomposition and/or autoencoders; experience in frameworks like