The Department of Chemistry and Materials Science is looking for:
A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians
Your role and goals
Your experience and ambitions
We welcome candidates with a Master's degree in (computational) chemistry, physics, or materials science who are curious about applied machine learning in the natural sciences. Prior machine learning or Python experience is a strong bonus, but not a must. We seek colleagues who enjoy coding, scripting and analytics, and are keen to push the boundaries of data-driven materials science and machine learning in atomistic simulations. This project requires creative thinking and programming, as well as technical expertise in materials simulations, electron–phonon physics, and machine learning. We further appreciate willingness to travel, teach and mentor, collaborate and communicate science.
To succeed in this role, you should have:
A Master’s degree (or equivalent*) in Chemistry, Physics, Materials Science, Mathematics, Computer Science, or a related field. (*You are required to have a degree that would allow you to enroll for a PhD program in the granting institution, e.g., a 1st hons BSc in the UK is also eligible.)
Prior programming experience, especially with Python. While you are not expected to be an expert programmer, some hands-on experience in programming is mandatory. Note that it will be entirely possible to develop more advanced programming skills during the doctoral studies.
A strong interest in atomistic simulations, machine learning and scientific method and software development.
Proficiency in English (written and spoken).
(Preferred) Experience with any of the following:
Electronic structure software (e.g., Quantum ESPRESSO).
Molecular dynamics packages (e.g., LAMMPS).
Machine learning interatomic potentials (e.g., GAP, MACE, NequIP ).
Machine learning libraries and frameworks such as Scikit-learn, TensorFlow, or PyTorch, and e3nn_jax.
If you have experience with other types of modeling tools (e.g., Boltzmann transport solvers, phonon codes like Phono3py/ShengBTE), please state it in your cover letter.
Applicants must fulfill the eligibility and admission criteria for Aalto’s Doctoral Programme in Chemical Engineering as specified at
What we offer
Aalto’s Department of Chemistry and Materials Science is a leading research environment in Finland for computational chemistry and materials science, with four groups specializing in different branches (Soft Materials Modelling, Computational Chemistry, Inorganic Materials Modelling, and Data-driven Atomistic Simulation).
The fixed term contract is initially for 2 years and during the first 6 months you must apply and receive a right to study in the doctoral programme. Aalto University follows the salary system of Finnish universities. The starting salary for Doctoral Researchers is 3142,65€ / month (gross). The contract includes Aalto University occupational healthcare benefits.
The position will be filled as soon as a suitable candidate is identified. The starting date for the position is in the autumn 2026, but the exact date can be agreed with the selected candidate. The primary workplace will be the Otaniemi Campus at Aalto University
A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians • Otaniemi, Espoo, Finland
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