Doctoral Researcher (PhD student) in Machine Learning

Euraxess; Espoo; On-site; EUR 30,132 - 40,176; Full time

Benefits offered by this job

Occupational healthcare

Right to study in doctoral programme

Salary according to Finnish university

Research environment access

Job summary

Aalto University is seeking a Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians. You will develop data-driven ML workflows, generate datasets from electronic structure calculations, and apply advanced AI frameworks to analyze thermoelectric transport and gas adsorption effects.

Join the ELPH-ML project and collaborate with experts in chemistry, physics, and materials science.

Qualifications

• Master's degree in Chemistry, Physics, Materials Science, Mathematics, Computer Science, or a related field.
• Prior programming experience, especially Python.
• Strong interest in atomistic simulations, machine learning and software development.
• Proficiency in English (written and spoken).

Responsibilities

• Develop data-driven and machine learning workflows to predict Wannier Hamiltonians, phonon properties, and electron–phonon coupling.
• Generate datasets from electronic structure calculations using Quantum ESPRESSO, Wannier90, and EPW.
• Apply the E(3)-equivariant AI framework to quantify band-convergence effects on thermoelectric transport and model gas adsorption effects.
• Manage large-scale simulations on supercomputing facilities and share results with experimental collaborators.

Skills

Python programming

English proficiency

Machine learning in materials science

Atomistic simulations

Data analytics

Education

Master’s degree in Chemistry/Physics/Materials Science/Math/CS or related field

Tools

Python

Quantum ESPRESSO

TensorFlow / PyTorch

Scikit-learn

e3nn_jax

Job description

Organisation/Company AALTO UNIVERSITY Research Field Chemistry Engineering Computer science Mathematics Physics Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Application Deadline 31 Aug 2026 - 00:00 (UTC) Country Finland Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

A Doctoral Researcher (PhD student) in Machine Learning for Electron–Phonon Interactions and Wannier-Based Hamiltonians

Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 16 000 students and 5 200 employees, including 446 professors. Our campus is in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

The School of Chemical Engineering (CHEMSchool) is one of the six schools of Aalto University. It combines natural sciences and engineering in a unique way.

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

The ELPH-ML project, led by Dr. Ransell D'Souza at the Department of Chemistry and Materials Science, Aalto University, and the Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro at the Department of Chemistry and Materials Science, Aalto University, are jointly hiring a Doctoral Researcher. In this position, you will work on a project funded by the Research Council of Finland to build a machine learning framework linking electron–phonon interactions, Wannier-based Hamiltonians, and phonon properties for functional materials. You will work under the supervision of the Principal Investigator, Dr. Ransell D'Souza, and collaborate closely with Prof. Miguel Caro's group, whose core expertise is the development of machine-learning-infused atomistic modelling techniques and their application to important problems in chemistry, physics and materials science. Together, you will help advance a key scientific discipline that directly impacts important technological and societal topics such as thermoelectric energy harvesting and next-generation gas sensors. The project has access to state-of-the-art supercomputing facilities (CSC's Puhti, Mahti, and LUMI) and is well integrated within the international electronic-structure and machine-learning communities. Informal inquiries about the position can be directed to Ransell D'Souza ([e-mail]). Please read the description below in full before directly contacting us by email.

Your role and goals

You will develop data-driven and machine learning workflows to predict Wannier Hamiltonians, phonon properties, and electron–phonon coupling in layered transition-metal dichalcogenides (TMDCs) such as MoS₂, WS₂, MoSe₂, WSe₂, and WTe₂. For training the machine learning models, you will generate datasets from electronic structure theory calculations using Quantum ESPRESSO, Wannier90, and EPW. You will apply the developed E(3)-equivariant AI framework to quantify band-convergence effects on thermoelectric transport (Seebeck coefficient, conductivity, ZT) and to model gas adsorption effects (NH₃, CO, CO₂) relevant to next-generation 2D gas sensors. You will manage large‑scale simulations run on world‑class supercomputing facilities alongside AI algorithms and data analytics tools, and share your results with experimental collaborators. The position is part of the Research Council of Finland project ELPH-ML (https://research.fi/en/results/funding/88752). In combination with academic development courses at Aalto University, we will help you grow a competitive and international career profile.

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:

• AMaster’sdegree(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‑hon 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).
• 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.

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 3 142,65 € per 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.