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Welcome to Quantiki, the world's leading social portal for everyone involved in quantum information science. No matter if you are a researcher, a student or a fan of quantum theory, this is the place you are going to find useful and enjoyable! While here on Quantiki you can: browse our content, including fascinating and educative articles, then create your own account and log in to gain more editorial possibilities.
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Submitted by
iperl on Fri, 17/01/2020 - 21:15.
The Applied Mathematics Department of Higher School of Economics invites applications for postdoctoral research position in mathematical methods for quantum information and quantum control technologies, see the description and requirements on https://iri.hse.ru/applied_math2 . The successful candidate will work closely with Professor Elena R. Loubenets and other members of the Department. The term of employment for this position will be for one year.
Submitted by
h.kwon on Fri, 17/01/2020 - 12:07.
We are seeking to appoint two Research Associates to work on the applications and/or architecture of quantum computers. The posts are funded jointly by the UK Quantum Hub on Quantum Computing and Simulation and the EPSRC Skills Hub in Quantum Systems Engineering.
Submitted by
h.kwon on Fri, 17/01/2020 - 12:03.
We are seeking to appoint a Research Associate to work on the newly established Samsung-Imperial project “Quantum Simulation for New Quantum Materials”.
Submitted by
flhdavid on Thu, 16/01/2020 - 11:23.
Junior Researcher Permanent Position in Theory of Quantum Physics and Quantum Information
Submitted by
ctahan on Wed, 15/01/2020 - 18:55.
The Quantum Information and Device Theory group of Charles Tahan at the Laboratory for Physical Sciences (LPS) and the Quantum Information and Many-Body Physics group of Brian Swingle at the University of Maryland (UMD) are collaborating to explore the possible quantum advantage offered by near-term quantum systems from quantum computers with dozens or hundreds of qubits to analog quantum systems of similar size to tensor-network inspired machine learning.
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