Doctoral Researcher in Stellar Dynamo Modelling and Exoplanet Discovery
Develop next-generation GPU-accelerated models of stellar dynamos to advance exoplanet research and space weather studies.
Best for:Best for computational astrophysicists or applied physicists with strong programming skills in C/C++/Fortran/Python and experience in high-performance computing, numerical methods, and machine learning for scientific applications.
Overview
This doctoral position at Aalto University’s Department of Computer Science focuses on advancing stellar dynamo modelling to improve exoplanet discovery and space weather prediction. The successful candidate will work within the PEAS consortium, collaborating with leading teams in stellar astrophysics, exoplanets, and high-performance computing. The role involves developing and implementing data-driven dynamo models, running large-scale simulations on cutting-edge HPC resources, and integrating machine learning techniques to interpret stellar magnetic activity. The position offers a unique interdisciplinary environment combining astrophysics, computational science, and high-performance computing, with opportunities for both academic and industry-relevant skill development.
What you'll do
- Develop and implement data-driven differential rotation constraints in global dynamo models using relaxation terms in the Navier–Stokes equation, calibrated against solar/stellar observations.
- Design, execute, and analyze large-scale global solar/stellar dynamo simulations capable of capturing both small-scale and large-scale dynamos with resolutions requiring LUMI-class HPC resources.
- Perform model selection and pruning using observational constraints, extend best-matching simulations over multiple stellar activity cycles, and prepare datasets for machine learning analysis.
- Contribute to coupling global and local radiative MHD models, including GPU-accelerated radiative transfer and high-cadence data extraction to study eruptive conditions and active region progenitors.
- Interface with SWMF modules (AWSoM/IA) to model stellar coronae and winds driven by dynamo-generated magnetic maps.
- Disseminate research findings through peer-reviewed journals and conferences, and contribute to open-science releases of data and code.
- Participate in teaching activities as per Aalto University’s standard practices for doctoral researchers.
Requirements
Degree required: Master’s degree in astrophysics, applied physics, computational science, or a closely related field, completed before the contract start date.
Degree offered: PhD
Language: English; proficiency required for research and communication (no explicit test score mentioned).
Instruction: English
- Strong programming skills in C/C++/Fortran and Python, with familiarity in reproducible workflows and version control.
- Relevant coursework in computational physics, scientific computing, numerical methods for PDEs, and high-performance computing (parallel/distributed programming, GPU programming).
- Experience in astrophysical fluid dynamics or magnetohydrodynamics (MHD).
- Knowledge of radiative transfer in astrophysics.
- Familiarity with machine learning or data analysis techniques for physical sciences.
- Motivation and ability to conduct independent and collaborative research, with strong communication skills in English.
- Must have completed a Master’s degree before the contract start date.
- No explicit nationality or residency restrictions mentioned.
Funding & terms
How to apply
Deadline: 2026-10-15 · open until filled
Submit a single PDF file containing a CV, cover letter (1–2 pages), degree transcripts, contact information for 1-2 referees, and links to relevant code repositories, theses, or preprints via Aalto University’s Workday recruitment system. Employees and visitors must use their existing Workday account to apply internally.
- CV
- Cover letter (1–2 pages)
- Degree transcripts
- Contact information for 1-2 referees
- Links to code repositories, theses, or preprints (if available)
Contact
Maarit Korpi-Lagg· Associate Professor (Research-related inquiries)
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