진학프로석·박사 채용 접수 플랫폼

Role Description

Artificial intelligence (AI) and quantum technologies are among the most transformative areas of frontier research and development in science and engineering today. Their convergence has the potential to redefine how we simulate, optimize, and understand complex physical systems. Integrating AI into quantum computers and simulators can help overcome current hardware constraints, improve system control, and unlock new modes of problem-solving that surpass classical approaches.

The ML-QSIM project is built upon a robust multi-node and multi-regional structure, encompassing four complementary research groups located in Asturias (CINN), Aragón (INMA), Catalunya (CVC), and Madrid (ICMM), and aims to develop AI-driven methodologies for quantum simulations and computation based on neutral atom platforms, exploring both theoretical and experimental domains. Research will span quantum control, quantum-enhanced machine learning, and hybrid quantum-classical computation, interfacing closely with cold-atom experiments under construction at CINN. Our goal is to create robust AI tools that enhance quantum performance, contribute to the emerging ecosystem of quantum technologies in Spain and the EU, and position our consortium at the intersection of quantum physics, computer science, and artificial intelligence.

We are seeking three highly motivated postdoctoral researchers to join the team. Each position will address a complementary research area within the project:

1. Quantum Control and Reinforcement Learning (CINN, Asturias)

• • Develop AI-driven control strategies based on reinforcement learning to optimize cooling and trapping parameters using real-time feedback for the Rydberg atom experimental platform.
• • Enhance holographic methods for optical tweezers with generative models and closed-loop optimization.
• • Benchmark AI-assisted methods against classical control approaches and assess scalability and robustness under realistic noise conditions.

2. Neural Ansätze for Quantum Simulations (INMA, Zaragoza)

• • Build and maintain software infrastructure for modeling quantum systems with machine learning tools.
• • Investigate neural-network-based representations of many-body quantum states (variational ansätze), symmetries, and thermal or dynamical properties.
• • Bridge the gap between simulations and experiments by connecting with the neutral atom platform developed at CINN.

3. Quantum Machine Learning and Quantum Optimization (ICMM, Madrid)

• • Develop theoretical frameworks for qudit-based quantum neural networks and quantum active learning (QAL).
• • Investigate hybrid quantum-classical pipelines, quantum compilation techniques, and noise-aware algorithms for Rydberg architectures.
• • Apply quantum optimization to real-world problems such as logistics, scheduling, and portfolio allocation, comparing performance with classical methods.

Qualifications

• • PhD in Physics, Computer Science, Applied Mathematics, or related fields.
• • Strong background in at least one of the following: machine learning, quantum computing, control theory, computer vision, or numerical simulations.
• • Experience with Python-based scientific programming
• • Excellent problem-solving skills,
• • Ability to work collaboratively in a multidisciplinary environment and communicate across different scientific cultures.
• • Fluency in English.

What We Offer

• • Competitive salary and contract terms following institutional and national regulations for up to three years.
• • A vibrant, interdisciplinary research environment in collaboration with leading Spanish quantum technology centers.
• • Access to quantum simulation platforms, HPC resources, and experimental facilities.
• • Opportunities to contribute to EU-funded research initiatives and international collaborations.
• • A commitment to open science, including software, data, and collaboration tools.

How to Apply

Please send the following materials to mlqsim2025@gmail.com by 28/02/2026

• • A cover letter indicating which position(s) you are applying for and your research interests
• • Curriculum vitae including publication list and (if available) access to contributed software repositories (github/gitlab)
• • Names and contact information of two references

Applications will be considered on a rolling basis until all positions are filled. More details of the hiring process will be given to pre-screened candidates.