Quantiki

The development of quantum technologies and machine learning are two of the most dynamic research directions of our time. Within the Helmholtz Young Investigator Group “Machine Learning for Quantum Technology” we aspire to merge the parallel advancements of both fields, where quantum challenges match the natural strengths of machine learning and, reversely, the quantum applications call for the development of new machine learning techniques. Therefore, our research targets the exciting intersection of (non-equilibrium) quantum matter, quantum information, and machine learning with the goal of unveiling previously unexplored many-body physics and devising interactive strategies to manipulate artificial quantum systems. The Young Investigator Group is part of the Peter Grünberg Institute - Quantum Control (PGI-8) at Forschungszentrum Jülich, which specializes in novel optimal control strategies for emerging quantum technologies. Current research directions include tensor network methods, machine learning, in-situ optimal control, and quantum many-body phenomena.

The School of Computational Sciences at Korea Institute for Advanced Study (KIAS) invites applicants for Project Research Fellow position for the following research field.

Research Field: Developing quantum algorithms for simulating open quantum dynamics

The School of Computational Sciences at Korea Institute for Advanced Study (KIAS) invites applicants for Project Research Fellow position for the following research field.

Research Field: Analyzing quantum entanglement under a noisy environment and developing active noise control schemes for quantum repeater

We're recruiting a postdoctoral researcher for a 2 year position to begin around August / September 2025. The successful candidate will work closely with Peter Brown and Cambyse Rouzé and join the rapidly growing Inria research team Quriosity located at Télécom Paris, Institut Polytechnique de Paris.

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.

We're advertising a 3-year PhD position in the QURIOSITY Group of Inria Paris Saclay, based at Télécom Paris.

The successful candidate will work closely with Mirjam Weilenmann at the intersection of quantum foundations and quantum Information theory. A master's degree in Mathematics, Physics or Computer science is required, expertise in quantum information, quantum technologies and/or convex optimisation is further desirable. The starting date should ideally be in Autumn 2025, but there is some flexibility.