Doctoral Researcher in quantum-inspired tensor network machine learning solvers for super-moiré van der Waals materials

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At the Department of Applied Physics, our pioneering research in physical sciences creates important industrial applications that hold great technological potential. Our research focuses on Materials physics, Quantum technology, Soft & living matter, and Advanced energy solutions. Topics extend from fundamental research to important applications. We educate future generations of research and development professionals, data specialists, technology experts, inventors, and scientists for industry and society.

We are looking for a Doctoral Researcher for Quantum-inspired tensor network machine learning solvers for super-moiré van der Waals materials.

Moiré van der Waals superlattices provides an ideal platform to engineer emergent quantum states, including correlated and topological phases. Super-moiré structures enable hierarchical length scales and quasiperiodic potentials, requiring modeling techniques capable of handling extremely large system sizes. In this project, we develop algorithms based on active-learning tensor-network tight-binding strategies that enable simulations of ultra-large aperiodic systems at unprecedented scales, both in equilibrium and out of equilibrium. The project will enable us to study collective modes, including excitons and plasmons, in topological and correlated states in super-moiré van der Waals heterostructures. Milestones include the development of real-space topological probes using the tensor-network formalism, the computation of collective quasiparticle excitations in super-moiré with tensor networks, the computation of transport in super-moiré systems, and the development of a quantum-inspired active Hamiltonian-learning from real-space spectroscopy for super-moiré systems. This interdisciplinary approach combines tensor-network frameworks, machine learning, and STM spectroscopy to enable scalable quantum material design.

Your role

You will develop novel solvers based on tensor networks to solve exceptionally large emergent quantum states, ranging from topological, superconducting, correlated and out of equilibrium systems. You will be working at the interface between quantum many-body physics, materials science and machine learning. You will be developing algorithms in Julia. The project will be co-supervised by Prof. Jose Lado and Dr. Anouar Moustaj.

Your experience and ambitions

We are looking for motivated candidates with interest in tensor networks, emergent states in van der Waals moire quantum materials, and machine learning. The work will require solid programming skills, especially in Python and Julia.

We expect the candidates to have:

• MSc degree in physics, materials science, chemistry, mathematics, computer science or a closely related field
• Strong interest in computational quantum many-body methods
• Solid programming skills
• Interest in quantum matter, machine learning and many-body physics
• Fluency in English is required (spoken and written). Finnish language is not required.

What we offer

• A meaningful and inspiring research environment ranked among the world’s best in quantum technologies
• Access to world-class research infrastructure in high performance computing
• The chance to collaborate with experts across quantum matter, quantum many-body physics and machine learning
• Support for professional development, including training, international conferences, and career coaching
• A vibrant, international campus community in Espoo, Greater Helsinki

The position of the Doctoral Researcher is initially filled for 2 years. The contract is continued for another 2 years after a successful midterm review. The annual workload of research and teaching staff at Aalto University is currently 1612 hours. Aalto University follows the salary system of Finnish universities. The starting salary for a Doctoral Researcher is approx. 3075€/month and it includes occupational healthcare.

The work is done in Finland, and the primary workplace will be the Otaniemi Campus at Aalto University. Life on the revitalized campus is vibrant, featuring stunning architecture, tranquil nature, and a variety of cafes, restaurants, and services, all complemented by excellent public transportation connections.

Join us!

Please submit your application including the attachments mentioned below as one single PDF document in English through our online recruitment system by using the link on Aalto University’s web page (click the "Apply Now” button).

(1)   Letter of motivation

(2)   CV

(3)   Contact details of one or two references

Please note: Aalto University’s employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their existing Workday user account (not via the external webpage for open positions). If you are a student or visitor at Aalto University, please apply with your personal email address (not aalto.fi) via Aalto University open positions.

The deadline for applications is May 17th, 2026, at 23:59pm (EEST/UTC +3).

For additional information, contact Prof. Jose Lado (jose.lado(at)aalto.fi) and Dr. Anouar Moustaj (anouar.moustaj(at)aalto.fi).

Aalto University reserves the right for justified reasons to leave the position open, to extend the application period, reopen the application process, and to consider candidates who have not submitted applications during the application period.

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About Finland 

Finland is a great place for living with or without family – it is a safe, politically stable and well-organized Nordic society. Finland is consistently ranked high in quality of life and was listed again as the happiest country in the world: World Happiness Report 2025 

For more information about living in Finland: Aalto Careers for International Staff.

References

[1] Tiago V. C. Antão, Yitao Sun, Adolfo O. Fumega, Jose L. Lado, ‘Tensor network method for real-space topology in quasicrystal Chern mosaics’, Phys. Rev. Lett. 136, 156601 (2026)

[2] Anouar Moustaj, Yitao Sun, Tiago V. C. Antão, Jose L. Lado, ‘Momentum-resolved spectral functions of super-moiré systems using tensor networks’, arXiv 2512.18397 (2025)

[3] Anouar Moustaj, Yitao Sun, Tiago V. C. Antão, Lumen Eek, Jose L. Lado, ‘Tensor-network methodology for super-moiré excitons beyond one billion sites’, arXiv 2603.02011 (2026)

[4] Ability to harness quantum speed gains now within sight after researchers solve massive simulation problem in a heartbeat, https://www.aalto.fi/en/news/ability-to-harness-quantum-speed-gains-now-within-sight-after-researchers-solve-massive-simulation.

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