"A Doctoral Researcher (PhD student) in Machine Learning for Surface Structures"

The Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro at the Department of Chemistry and Materials Science, Aalto University, is 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 tool for predicting the atomic structure of surfaces. You will work under the supervision of the project leader, Academy Research Fellow Dr. Ondřej Krejčí, and collaborate with international experts in materials and surface science. Together, you will help advance this key scientific discipline, which directly impacts such important technological and societal topics as semiconductor devices and heterogeneous catalysis for green fuel production. You will be fully integrated within the DAS group, a vibrant and diverse research environment whose core expertise is the development of machine-learning-infused atomistic modeling techniques beyond the state of the art and their application to study important problems in chemistry, physics and materials science. The group has access to state-of-the-art supercomputing facilities (like LUMI) and is very well integrated within the scientific community internationally. More about our activities: https://miguelcaro.org.

Your role and goals

You will develop data-driven and machine learning workflows for predicting how atoms rearrange on the surface of materials such as metal oxides and semiconductors. Knowledge of the precise atomic structure is important for understanding and predicting electronic and catalytic properties and thus to engineer the material surfaces better for their technological usage. For training the machine learning models, you will generate datasets from electronic structure theory calculations. You will manage large-scale simulations run on world-class supercomputing facilities alongside AI algorithms and data analytics tools. You will also discuss tool development with AI and machine learning experts at Aalto University and share your results with experimental collaborators. The position is part of the Research Council of Finland project Towards Realistic Surface Structures (https://research.fi/en/results/funding/86328). A secondment to the group of Prof. Gareth Parkinson at the Technical University of Vienna is planned. In combination with the 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, machine learning and a broad understanding of surface and material science. 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., VASP, GPAW, FHI-aims).
• Molecular dynamics packages (e.g., LAMMPS, GROMACS).
• Machine learning interatomic potentials (e.g., GAP, MACE, ACE).
• Machine learning libraries and frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• If you have experience with other types of modeling tools (e.g., FEM), 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 Aalto Doctoral Programme in Chemical Engineering | Aalto University.

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 with a possible 2-year extension after passing the midterm review. 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 3075 € / 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 Spring 2026, but the exact date can be agreed with the selected candidate. The primary workplace will be the Otaniemi Campus at Aalto University.

Ready to apply?

To apply for the position, please submit your application no later than 8.1.2026 including the attachments mentioned below as one single PDF document in English through the link ’Apply now’ link at the bottom of the web page.

• Letter of motivation (max 1 page): Include your name and email. Briefly motivate your interest in the position and explain how/to what extent you fulfill the requirements. Briefly mention any prior research experience you may have. Please, do not use ChatGPT or similar tools to prepare your cover letter for you.
• CV including list of publications (max 2 pages): personal and academic information, list of skills, projects, etc. Lying on your CV is immediate grounds for disqualification. If you are invited for an interview, you will be asked about information provided here. If you are eventually offered the position, you will be asked for a transcript of academic records.
• Contact details of at least two referees (or letters of recommendation, if already available)

Applications sent via email will not be considered; only submissions through the online recruitment system are accepted.