PhD Position in AI-driven Computational Modelling of Quantum Materials and Qubits at ICN2 Barcelona

This fully funded PhD position at the Catalan Institute of Nanoscience and Nanotechnology (ICN2) in Barcelona offers an exciting opportunity to work at the intersection of quantum technologies, artificial intelligence, and advanced materials modelling. The project focuses on developing AI-driven computational workflows for modelling semiconducting spin qubits, a key component in quantum computing technologies. The research is embedded in a vibrant international network, with collaborations involving leading experimental teams at the Niels Bohr Institute (Denmark), IST Austria, QuTech/TU Delft (Netherlands), and the ICN2 experimental group of J. Arbiol. The technical objective of the thesis is to create a comprehensive computational workflow that integrates DFT-based materials databases, machine learning–derived interatomic potentials (notably MACE potentials), and automated Hamiltonian generation. These tools will be used in conjunction with the group’s in-house quantum transport code (LSQUANT) to explore electronic and spin properties in digital qubit models reconstructed from experimental data. The project aims to provide quantitative assessments of qubit figures of merit using tight-binding models and complementary codes for electrostatic environments and screening effects. The outcome will be a multiscale framework bridging atomic-scale imaging, first-principles modelling, and device-level simulation, offering new insights into scalable spin-based quantum technologies. The position is supervised by ICREA Research Professor Stephan Roche and José-Hugo García, both part of the Theoretical & Computational Nanoscience Group at ICN2, which is internationally recognized for its expertise in quantum materials, device modelling, and large-scale simulations. The group’s research covers quantum charge and thermal transport, spin dynamics, topological matter, graphene, 2D materials, and van der Waals heterostructures, with a strong emphasis on integrating AI and machine learning for predictive multiscale simulation. Eligibility requirements include compliance with the mobility rule (not residing in Spain for more than 12 months in the last 36 months), possession of a Master’s degree in quantum science and technology, condensed matter physics, or computational science, and no prior doctoral degree. Experience in Python coding and machine learning techniques is highly desirable. Applicants must provide proof of English proficiency (TOEFL 72, IELTS 5-6.5, CEFR B2, etc.), unless their previous degrees were conducted entirely in English. Required application materials include a CV (max 4 pages), motivation letter (max 2 pages), two recommendation letters, BSc and MSc transcripts, and a mobility declaration. The application process is transparent and fair, with a multi-phase evaluation including eligibility check, scientific merit review, interviews, and final ranking. Female candidates are strongly encouraged to apply. The fellowship is fully funded, with flexible start dates between October and December 2026. The program supports international researchers and promotes equality and non-discrimination. For more details and to apply, visit the PHYNEST online application platform before the February 28, 2026 deadline.