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. The successful candidate will be joining the Atomic Quantum Optics group led by Prof. Dr. Morgan Mitchell, in an experiment on the interaction of single photons and entangled photon pairs with individual trapped
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FieldPhysicsEducation LevelPhD or equivalent Skills/Qualifications 1. Strong background in High-Energy Physics Phenomenology (Flavour Physics advantageous but not mandatory). 2. Proficiency in computational methods
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the architecture of computing systems with special emphasis on graphics processors. Extend and improve evaluation tools. Characterize current solutions in terms of performance and energy consumption. Design, model
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performance with classical methods. Qualifications PhD in Physics, Computer Science, Applied Mathematics, or related fields. Strong background in at least one of the following: machine learning, quantum
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microscope, scanning tunnelling microscope, etc For more information about the QTWIST Program: https://qtwist.icfo.eu . Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=890
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Research Framework Programme? Not funded by a EU programme Reference Number CEX2024-001476-S Is the Job related to staff position within a Research Infrastructure? No Offer Description Prof. Chiara Amorino
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Apr 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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. The successful candidate will be joining the Atomic Quantum Optics group led by Prof. Dr. Morgan Mitchell. Searches for new particles and forces using table-top experiments is an expanding frontier of fundamental
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. The successful candidate will be joining the STM on 2D Quantum Materials Group led by Prof. Dr. Carmen Rubio-Verdú. MAIN RESPONSIBILITIES: Design and fabrication of devices Measurements in STM Data analysis
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. The successful candidate will be joining the STM on 2D Quantum Materials Group led by Prof. Dr. Carmen Rubio-Verdú. MAIN RESPONSIBILITIES: Design and fabrication of devices Measurements in STM Data analysis