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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
such as on-chip optical routing, flexible optoelectronics, light emission, optical sensing and quantum communications. This is an ambitious research program, with a strong interdisciplinary nature, across
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development of new 3D nanoscale thermal metamaterials. If you want to play at the nexus of quantum, advanced materials, and thermodynamics, to design and create intricate nanoscale geometries that allow you to
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-performance materials across multiple sectors. The disruptive nature of recent advances in artificial intelligence (AI), robotics, and emerging quantum computing offers timely and exciting opportunities for PhD
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are: identifying signatures of quantum-interference on ionization spectra due to coherent excitation and ionization, and obtaining the photoionization time delays of inner-shell electrons in multi-centre molecules
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quantum mechanical calculations with continuum micromagnetic theory to bridge atomic and macroscopic length scales. Developing a predictive, fundamental theory linking stress to measurable magnetic signals
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Description This exciting PhD project offers the opportunity to work at the forefront of biomedical engineering, developing the next-generation technologies to revolutionise long-term heart monitoring. You will
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extraction materials. Addressing these challenges requires fundamentally new approaches that integrate mineral liberation, molecular recognition, and process engineering. This PhD project contributes
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priorities, particularly Advanced Materials, Quantum and Emergent Phenomena, and Digital Futures. It addresses the discovery and control of novel material functionalities, supports high-risk and high-reward
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This exciting opportunity is based within the Advanced Manufacturing Technology Research Group (AMTRG), which leads cutting-edge manufacturing research with a world-unique Omnifactory facility
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio