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CNRS Junior Professor Chair in Quantum Science at the intersection
between Physics and Computer Science.
Submitted by
mplavala on Mon, 28/04/2025 - 16:18.
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.
Submitted by
dobrautz on Fri, 24/01/2025 - 23:33.
Application deadline:
Friday, February 28, 2025
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.
Submitted by
dobrautz on Fri, 24/01/2025 - 23:33.
Application deadline:
Friday, February 28, 2025
Submitted by
mgbukov on Sun, 29/09/2024 - 13:25.
The Nonequilibrium quantum dynamics group (https://www.pks.mpg.de/nqd ) at the Max Planck Institute for the Physics of Complex Systems (https://www.pks.mpg.de/) in Dresden (Germany) is looking to hire a PhD student to work at the intersection of quantum many-body physics and reinforcement learning (RL).
Submitted by
anazir on Thu, 29/02/2024 - 12:30.
The Department of Physics and Astronomy is seeking to appoint a new permanent lectureship (assistant professor equivalent) in Quantum Machine Learning, at the intersection of physics, quantum information, quantum technology, and machine learning (ML). Applicants from any area of quantum information will be considered.
Submitted by
mgbukov on Fri, 10/03/2023 - 15:04.
We have openings for PhD positions in the Nonequilibrium Quantum Dynamics group at the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany.
The research topics are in quantum many-body physics, and range from:
Our research lies at the intersection of many-body dynamics, quantum simulation, quantum control, and applications of machine learning in physics. We are interested in problems of both fundamental nature and immediate applications. We develop approximate analytical methods, and design numerical techniques in order to investigate different problems in quantum dynamics. We collaborate with theory groups and experimental labs to test our theoretical predictions against experiment.
Application deadline:
Monday, November 15, 2021
Postdoc and PhD positions in theoretical physics:
Machine Learning for Quantum Technologies
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