19 atomic-force-microscopy PhD positions at Chalmers University of Technology in Sweden
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electron microscopy is a strong advantage. What you will do Develop and apply advanced microanalytical techniques to accurately characterise recycled aluminium alloys Develop new knowledge on how the tramp
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mechanical analysis, nanonindentation and atomic force mircroscopy will be used to characterize the mechanical properties of (doped) conjugated polymers. You will work closely with fellow PhD students and
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the right one for you! This is a fully funded PhD position to develop micromechanical models of high-pressure die-cast aluminium, a unique opportunity for a motivated individual to work in a collaborative
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of terahertz electronics. In this role, you will work closely with world-class research groups and industrial collaborators, benefiting from state-of-the-art facilities. Your research will push the boundaries
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to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how these forces affect the degradation and lifetime of the machines. About the position The position is based
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that utilize streets and roads to manage stormwater, there is limited knowledge on effective design and implementation strategies. This PhD project aims to investigate how urban roads can be designed and
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We are looking for a PhD candidate fascinated in modelling erosion processes in sensitive clay slopes. The highly sensitive clays, called quick clays, can change from solid to liquid with small
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consensus mechanisms, and designing scalable defense strategies that uphold privacy and security guarantees. The candidate will work at the intersection of systems, networking, and security, contributing both
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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generalized, cross-layer defense framework that integrates network-level mitigation and application-level optimization to comprehensively protect distributed AI training from network threats while maintaining