PhD in ML for Electron–Phonon Physics in 2D Materials

Aalto University; Espoo; On-site; EUR 31,915 - 38,228; Full time

Benefits offered by this job

Occupational healthcare

Fixed-term contract

Job summary

Aalto University in Espoo invites applications for a Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier‑Based Hamiltonians. The role links ML, atomistic simulations, and advanced electronic-structure methods.

The ELPH‑ML project collaborates with the DAS group to build data‑driven ML workflows, using Quantum ESPRESSO, Wannier90 and EPW, with supercomputing resources and experimental partners. The position is fixed‑term and based at Otaniemi Campus.

Qualifications

• Master’s degree in Chemistry, Physics, Materials Science or related field.
• Prior programming experience, especially with Python; advanced skills can be developed during doctoral studies.
• Strong interest in atomistic simulations, machine learning and scientific software development.
• Proficiency in English (written and spoken).

Responsibilities

• Develop data-driven ML workflows to predict Wannier Hamiltonians and phonon properties.
• Generate datasets from electronic-structure calculations using Quantum ESPRESSO, Wannier90 and EPW.
• Apply E(3)-equivariant AI framework to quantify band-convergence effects on transport and model gas-adsorption for 2D sensors.
• Manage large-scale simulations on supercomputing facilities and collaborate with experimental partners.

Skills

Python

Machine learning

English proficiency

Data analysis

Education

Master's degree in Chemistry, Physics, Materials Science or related field

Tools

Quantum ESPRESSO

Wannier90

EPW

e3nn_jax

Scikit-learn

PyTorch

Job description

Aalto University in Espoo invites applications for a Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier‑Based Hamiltonians. The role links ML, atomistic simulations, and advanced electronic-structure methods.

The ELPH‑ML project collaborates with the DAS group to build data‑driven ML workflows, using Quantum ESPRESSO, Wannier90 and EPW, with supercomputing resources and experimental partners. The position is fixed‑term and based at Otaniemi Campus.