The position will be in the group of Prof. Robert Raußendorf and under the leadership of Martin Plávala. You will be working on using machine learning to construct verifiable witnesses of entanglement and quantum nonlocality. The goal of the project is to construct such witnesses in high-dimensional cases where other methods fail due to a lack of computational resources. The applications will include witnessing entanglement of quantum states, witnessing high-dimensional steering, and witnessing nonlocality in Bell scenarios and quantum networks.

The University’s Department of Physics invites applications for a PhD studentship funded by the Royal Society and the University of Exeter to commence on 1st October 2025 or as soon as possible thereafter. For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend for 4 years full-time, or pro rata for part-time study.

Quantum computing offers a transformative approach to materials simulation, potentially performing quantum-mechanical calculations orders of magnitude faster and more accurately than classical methods on conventional high-performance computers. This unlocks the true computational design of novel materials, paving the way for solutions to global challenges related to sustainability and renewable energies. This capability can revolutionize various fields, enabling, for example, the improvement of catalytic processes and a deeper, atomistic understanding of material degradation. These advancements pave the way for knowledge-based optimization of load, process, and operating conditions in devices like fuel cells, ultimately reducing our reliance on scarce, environmentally harmful, and expensive materials. The focus of the PhD thesis is the development and testing of hybrid quantum-classical workflows and relevant models for simulating industrially relevant materials on a near-term quantum computer.

Quantum computing is at the cutting edge of technological innovation, offering the potential to solve complex problems that classical "binary" computers cannot address. Tensor algebra, with its comprehensive mathematical framework, offers crucial tools for modeling and approximating large multidimensional datasets. This thesis seeks to investigate the interplay between tensor networks and quantum computing by proposing original, robust (to decoherence of qubits) quantum algorithms that utilize tensor structures to improve computational efficiency and capabilities. This research requires a multidisciplinary understanding of quantum physics and linear algebra. This thesis topic will benefit from the complementary expertises of Remy Boyer (CRISTAL/SIGMA) for the multilinear algebra aspect and Giuseppe Patera (PhLAM, Quantum Information team) for the quantum physics aspect.

We are looking for a PhD candidate to contribute to the development of theoretical methods for studying reactive charge-transfer processes. When oppositely charged ions collide, the resulting dynamics cannot be fully described within the Born-Oppenheimer approximation. In collisions involving molecular ions, not only does charge transfer occur, but molecular bonds may also break or form. These processes are crucial for understanding how molecules are created and destroyed in the interstellar medium, yet our current knowledge of them remains limited.