47 postdoc-density-functional-theory PhD positions at Chalmers University of Technology
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Join us for a fully funded PhD position in theoretical machine learning to uncover how and why transformers work. Explore their inner mechanisms using information theory. As part of this project
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search is nuclear theory; a research area in which we have a strong international position and benefit from exceptional collaborations. Organizationally we are part of the Division of Subatomic, High
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of computational solid-state materials research! As a PhD student in the Condensed Matter and Materials Theory division , you will have the opportunity to explore surface and device physics, using density functional
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Group focuses on framework materials, such as covalent organic frameworks (COFs). By combining chemistry and engineering strategies, we develop functional frameworks with versatile built-in nanochannels
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We are offering a PhD position in the field of algorithmic graph theory. The position is a full-time employment with a competitive monthly salary and full social benefits for up to five years. You
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senior researchers, three postdocs and three PhD students. It is embedded in an interdisciplinary environment where we have close collaboration with other research teams at Chalmers such as technology
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and development. Project overview The ongoing transition from synchronous machine-based generation to converter-dominated renewable energy sources presents new challenges for the operation, control, and
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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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of waveforms for satellite communication and radar systems communication/radar system performance analysis using theory and simulation field tests in relevant operating conditions retrieval of geophysical
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real-world impact. We work across domains such as healthcare, automotive, and manufacturing, using multimodal data and foundation models. The team includes PhD students, postdocs, and industry