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. This position reinforces 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 role builds on expertise in machine‑learned interatomic potentials, large‑scale molecular dynamics, and tight‑binding/electronic‑property modelling of a‑BN, extending to foundation AI models for materials, machine‑learning potentials, and machine‑learned Hamiltonians validated against DFT and experimental 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 (37.5 h/week).
- Salary will depend on qualifications and demonstrated experience.
- Support to relocation issues.
- Life insurance.
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.
#J-18808-Ljbffr