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Field
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PhD in secure multiparty computation in quantum networks Apply Vacant position PhD in secure multiparty computation in quantum networks Aalborg Deadline : 13.09.2026 Department: Department of
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-edge research in: - Condensed Matter Theory - Quantum Information & Quantum Computation - Topological Physics & Quantum Geometry - String Theory and its connection to Condensed Matter Our
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Inria, the French national research institute for the digital sciences | Villers-lès-Nancy, Lorraine | France | 15 days ago
computation is deterministic in a suitable sense known as ‘robust determinism’. Translation between different models of quantum computing (such as quantum circuits or the one-way model) are necessary to
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quantum-resistant cryptography Secure (side-channel resistant) and efficient implementation of quantum-resistant cryptography Secure computation protocols and their applications (e.g. to private
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, quantum matter, and quantum computation. The initial appointment is for two years, with the possibility of renewal or promotion to a senior research position contingent on mutual agreement and
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Chemistry, Data Sciences and Machine Learning, Quantum Information and Quantum Computation. There are currently a few open positions at all levels (from postdoctoral fellow to full professor) in Quantum
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include (but not limited to) topological phases of matter and their realizations on quantum devices, quantum error correction, fault-tolerant quantum computation, non-equilibrium quantum dynamics, and
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field of quantum information and computation . Selected position shall begin in 2026 but actual start date and salary are negotiable. The Quantum Information Science and Engineering (QISE) Department at
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Assurance and Insurance, Quantum Trust, Privacy-preserving Machine Learning, Privacy-preserving Multi-party Computation, Verifiable Computation, Trustworthy Systems, Adversarial Machine Learning
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Assurance and Insurance, Quantum Trust, Privacy-preserving Machine Learning, Privacy-preserving Multi-party Computation, Verifiable Computation, Trustworthy Systems, Adversarial Machine Learning