413 high-performance-quantum-computing-"https:"-"https:"-"https:"-"https:" Fellowship positions in United Kingdom
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Field
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Hardware Implementation”. This project is funding by the EU funding schema CHIST-ERA and comprises a European consortium. This consortium is led by the Centre for Secure Information Technologies (CSIT: https
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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, and protection schemes Perform power system studies such as load flow, short-circuit, and protection coordination Operate and control high-power or high-voltage equipment Lead testing, commissioning
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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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. Please visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more. Our commitment to Equality, Diversity and Inclusion UCL Computer Science is proud to be the first computer
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candidate will perform computational calculations to design various nanophotonic and nanoplasmonic devices that allow for quantum entanglement and quantum interferometry to be realized. The successful
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The School of Mathematical Sciences invites applications for a Research Fellow position in the field of theoretical high-energy physics. This is a 2-year position envisioned to start in October 2026
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About us The Max Planck UCL Centre for Computational Psychiatry and Ageing Research (MPC) is dedicated to studying the causes of psychiatric disorders as well as the causes of individual
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to make a meaningful impact? Mathematics & Computer Science - Postdoc Fellows E-STEEM Call 2026 174 Research Services and Career Development Startdate: 01.10.2026 | Working hours: 40 | Collective bargaining
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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