As a flagship research center in nanoscience and nanotechnology, our mission is to open and explore new frontiers of knowledge at the nanoscale, and bring value to society in the form of new understanding, capabilities and innovation, while inspiring and providing broad training to the next generations of researchers. Our values are Commitment, Collaboration and Transformation.

Our research lines focus on the newly-discovered physical and chemical properties that arise from the behaviour of matter at the nanoscale. ICN2 has been awarded with the Severo Ochoa Center of Excellence distinction for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different areas of nanoscience and nanotechnology.

Job Title: Postdoctoral Researcher in AI-enabled atomistic and electronic modelling of amorphous boron nitride for interconnect technologies

Department: Theoretical and Computational Nanoscience

Description of Group/Project:

The Theoretical and Computational Nanoscience Group develops predictive atomistic and electronic-structure methodologies for low-dimensional, disordered and quantum materials. The proposed position will reinforce the group's work on amorphous boron nitride (a-BN) as an ultra-low-k intermetal dielectric and diffusion-barrier material for future semiconductor interconnects. The activity builds on the group expertise in  machine-learned interatomic potentials, large-scale molecular dynamics, and tight-binding/electronic-property modelling of a-BN compounds, and extends it towards foundation AI models for materials, machine-learning potentials, and machine-learned Hamiltonians validated against DFT and experimental/industrial data.

Main Tasks and responsibilities:

• Benchmark and adapt emerging foundation AI/materials models and ML potentials (e.g. MACE, NequIP/Allegro, ORB, CHGNet/MatGL, GAP baselines) for B-N-C-H-O systems.
• Generate and curate DFT-quality reference datasets for pristine and contaminated a-BN, including C, H and O incorporation, voids, density fluctuations, interfaces and thick-film motifs.
• Run large-scale molecular dynamics to simulate growth, annealing, densification and crystallisation pathways in sub-10 nm to >50 nm a-BN film models relevant to BEOL/damascene integration.
• Build workflows coupling ML potentials to machine-learned Hamiltonians/tight-binding models, calibrated to DFT on representative cells, to obtain electronic structure, DOS/IPR, localization and dielectric response.
• Compute composition- and morphology-dependent dielectric constants, breakdown-related descriptors, leakage-risk indicators and Cu/Co diffusion-related barrier descriptors.
• Analyse how contamination and thickness-driven crystallisation affect ultra-low-k performance and reliability; deliver design rules for stable, dense, non-porous a-BN IMD films.
• Interface with experimental partners and convert predictions into testable synthesis/characterisation hypotheses; prepare publications, data workflows and code documentation.

Requirements:

• Education: PhD in physics, materials science, computational chemistry, electrical engineering or a related area.
• Knowledge:

Atomistic simulations, DFT, molecular dynamics, machine-learned interatomic potentials and/or foundation models for materials, tight-binding/electronic-structure methods, dielectric/transport descriptors, Python/HPC workflows, disordered materials and semiconductor interconnect materials.

• Professional Experience:

Demonstrated experience in ML-assisted modelling of amorphous BN or related multicomponent amorphous systems; publications in materials/nanoelectronics; experience interacting with experimental collaborators and benchmarking against measurements.

• Personal Competences:

Autonomy, scientific creativity, rigorous benchmarking, collaborative mindset, capacity to translate computational results into materials-design rules, and ability to produce high-quality publications and reusable workflows.

Summary of conditions:

• Full time work (37,5h/week)
• Contract Length: Temporary
• Location: Bellaterra (Barcelona)
• Salary will depend on qualifications and demonstrated experience.
• Support to the relocation issues.
• Life Insurance.

Estimated Incorporation date: as soon as possible

The position is justified by a clear scientific and technological need identified in the future-interconnect roadmap: a-BN has compelling ultra-low-k and mechanical/barrier properties at sub-10 nm thickness, but thicker films needed for IMD applications suffer from changes in composition, density and crystallinity. The project therefore focuses on AI-based simulation workflows capable of treating realistic scales and non-idealities (defects, roughness, interfaces and chemical inhomogeneity) and translating atomistic descriptors into electronic/dielectric device-relevant models.

Grant PID2022-138283NB-I00 funded by MICIU/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe".

Funded by the European Union's Horizon Europe research and innovation programme - European Research Council Executive Agency under grant agreement No 101078370 - AI4SPIN

How to apply:

All applications must be made via the ICN2 website and include the following:

1. A full CV including contact details.
2. 2 referee contacts.
3. 2 Reference letters or referee contacts.

Deadline for applications: 18^th May 2026

Equal opportunities:

At ICN2, we foster an inclusive and safe work environment, free from any form of discrimination-whether based on gender, sexual orientation, gender identity, age, origin, culture, religion, disability, or any other personal or social condition. We are committed to ensuring equal treatment and opportunities in all our processes, especially in recruitment, which is based solely on talent, experience, and ability. We implement proactive policies for inclusion and harassment prevention that reinforce our commitment to respect and fairness. If you share these values and are looking to grow in an open and diverse environment, ICN2 is ready to welcome you.

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