machine learning

We invite applications for a postdoctoral associate position in theoretical quantum information science in the Physics Department of Virginia Tech.

The successful candidate will work in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending availability of funding.

We are looking for a highly motivated candidate for a fully-funded interdisciplinary PhD position between quantum computing, condensed matter physics and quantum chemistry, starting in either spring or fall 2026, at Université de Montréal (UdeM), in the vibrant city of Montréal in Québec, Canada!

Supervisors: Prof Michel Côté (Physics), Prof Matthias Ernzerhof (Chemistry) and Prof Hlér Kristjánsson (Computer Science) at the University of Montréal and Mila – Québec AI Institute

Collaboration with Dr Simon Verret, quantum developer at Université de Sherbrooke

The School of Computational Sciences at Korea Institute for Advanced Study (KIAS) invites applicants for the positions at the level of postdoctoral research fellow in Computational Sciences.

Our group builds artificial intelligence systems for discovering new concepts, experiments and ideas in physics.

To accelerate this effort, we need your help!

We have several fully-funded open PhD and Post-Doc positions (m/f) at the University of Tübingen, one of Europe’s most vibrant hub for artificial intelligence research.

A list of concrete potential projects:
• Development of modern auto-differentiation (JAX-based) physics simulators for the discovery of new physics experiments

CNRS Junior Professor Chair in Quantum Science at the intersection between Physics and Computer Science.

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 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.

We offer a fully funded 3-year PhD Student (f/m/d) in Machine Learning for Quantum Computing and Simulation of Quantum Matter. The successful PhD candidate (f/m/d) will be part of the interdisciplinary DRESDEN-concept Research Group AI 4 Quantum which is currently established at the Center for Advanced Systems Understanding (CASUS) of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI) at the Center for Interdiscip­linary Digital Sciences (CIDS) of the TU Dresden. In this project, you will learn to develop and apply novel deep machine learning (ML) methods, neural network and artificial intelligence approaches to extend the reach of accurate computational methods to study strongly correlated quantum matter. The focus will be the development of novel Neural Quantum States, deep learning approaches to design quantum computing (QC) algorithms, and AI and ML techniques to improve state-of-the-art quantum Monte Carlo methods.

We offer a two-year Postdoc (f/m/d) in Machine Learning for Quantum Computing and Simulation of Quantum Matter. The successful candidate (f/m/d) will be part of the interdisciplinary DRESDEN-concept Research Group AI 4 Quantum which is currently established at the Center for Advanced Systems Understanding (CASUS) of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI) at the Center for Interdiscip­linary Digital Sciences (CIDS) of the TU Dresden. You will develop and apply novel deep machine learning (ML) methods, neural network and artificial intelligence approaches to extend the reach of accurate computational methods to study strongly correlated quantum matter.