50 high-performance-quantum-computing-"https:"-"https:" Fellowship research jobs at University of Birmingham
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the underground infrastructure theme within the Quantum Technology Research Hub in Sensors, Imaging and Timing (www.quisit.org ). The successful candidate will focus on designing and conducting large scale
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Job Description Position Details School of Computer Science Location: University of Birmingham, Edgbaston, Birmingham UK Full time starting salary is normally in the range £36,636 to £46,049 with
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at the intersection of nanophotonics, plasmonics, and quantum optics, while contributing to a high-impact research programme aimed at advancing next-generation photonic and quantum technologies. Main Duties
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2026 Background To create and contribute to the creation of knowledge by undertaking a specified range of activities within an established research programme and/or specific research project
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Job Description Position Details School of Computer Science, College of Engineering and Physical Sciences Location: University of Birmingham, Edgbaston, Birmingham UK Full time starting salary is
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) in computational materials chemistry/physics, or related relevant area. The candidate must have demonstrable experience working with high-performance computing infrastructure. The candidate must have a
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Github • Using a range of computer systems to run fluid flow simulations and optimisation algorithms, including High Performance Computing architectures • Assist and mentor students and research group
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future of formulated polymers. We are seeking a Research Fellow in Computational Chemistry and AI/Machine Learning to advance the state of the art in understanding the degradation and biodegradation
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candidate has either analytical or computational skills (exact diagonalisation or tensor network techniques) and research experience in either many-body quantum physics, statistical mechanics, statistical
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during