23 signal-processing Postdoctoral positions at Chalmers University of Technology in Sweden
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processes for CO₂ hydrogenation as part of CCU. The objective of the project is to develop novel catalysts for the process that will be examined in high pressure CO2 hydrogenation experiments, combined with
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resources for your research. Project overview The project aims to explore new catalytic processes for CO₂ hydrogenation as part of CCU. The objective of the project is to develop novel catalysts
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edge research in materials design, processing, and advanced characterization. We promote interdisciplinary collaboration and sustainability focused research, with strong ties to both academic and
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symbolic computer algebra systems such as Mathematica is required You will need strong written and verbal communication skills in English *The date on your doctoral degree certificate is considered
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amplifier performance. By combining advanced device measurements, empirical modeling, and power amplifier design, this project will generate new insights into the material, process, and design factors
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fluid dynamics and vascular modeling in microenvironments Skills in data analysis and image processing (e.g., Python, R, ImageJ) Ability to mentor junior researchers and contribute to team leadership What
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investigations are also diverse and complementary, and range from theory and computer simulations to experiments in subatomic physics. The Plasma Theory group within the Division conducts research on acceleration
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your native language, Chalmers offers Swedish courses to help you settle in. Application procedure The application should be written in English be attached as PDF-files, as below. Maximum size for each
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simulations to experiments in subatomic physics. The Plasma Theory group within the Division conducts research on acceleration and radiation generation in magnetic fusion, laser-produced and astrophysical
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC