Description The is a postdoctoral position (R2) for 3 years, starting asap (before end december 2026), focused on ‘Quantum Machine Learning’, with the objective of investigating hybrid classical-quantum algorithms, suitted for rsearchers with background in Quantum Information or Quantum Computing, and Machine Learning.

More details:

https://www.cvc.uab.es/blog/2026/07/29/postdoc-position-on-quantum-machine-learning-2/

Contract person:

Prof. Fernando Vilariño ([email protected]), head researcher at the QML-CVC Group (http://qml.cvc.uab.es)

Candidate's profile The candidate should possess a PhD in Quantum Information or Quantum Computing at the time of enrollment, and have experience and skills in algorithm design and implementation. The candidate should have a background in machine learning and experience is welcome in common software libraries such as Pennylane, Qiskit or similar. Applicants are expected to be fluent in both oral and written communication in English.

They should mandatory work well in an interdisciplinary team while demonstrating initiative and independence. The candidate is expected to supervise PhD students.

Responsibilities The tasks will include the design and implementation of explorative research pathways for general problems related to quantum machine learning. Specific research lines can be defined in collaboration with the supervisor, with potential avenues including generative models, optimization tasks, or other specific applications. The researcher will supervise PhD Students and Master students, and contribute to the state of the art with research papers to journals and conferences.

Qualifications





PhD in Quantum Information or Quantum Computing or equivalent at the moment of the enrolement.

Wider Research context

This contract will be part of project I D i grant AIA2025-163435-C43, funded by MICIU/AEI/10.13039/501100011033/. The “Machine Learning for Quantum Simulations with Rydberg Atoms (ML-QSIM)” project addresses a central challenge in the current quantum revolution: the integration of advanced Artificial Intelligence and Machine Learning (AI/ML) techniques with Rydberg-atom quantum simulators, one of the most promising architectures for scalable quantum technologies. The coordinated initiative brings together four leading research nodes –CINN (Asturias, coordinator), INMA (Aragon), ICMM (Madrid), and CVC (Catalonia)– to simultaneously advance quantum hardware, theoretical modeling, hybrid simulation frameworks, and quantum algorithms tailored to next-generation analog quantum devices.

The QML-CVC team is responsible of the subproject QuCV, with the responsibility of developing exploratory research pathways in computer vision through the use of quantum machine learning (QuCV) This provides a singular oportunity for the CVC to develop a research agenda in the context of quantum computation. The proposal responds to a strategic scientific need: to identify where quantum devices can outperform state-of-the-art classical and AI-enhanced methods, to improve the stability and performance of emerging quantum platforms, and to develop quantum algorithms with real applicability in fields such as combinatorial optimization, quantum chemistry, complex materials, and computer vision.

📌 Postdoc Position in Quantum Machine Learning at QML-CVC (Barcelona)
🏢 Computer Vision Center
📍 Barcelona