Summary
The Department of Computer Science at Aalto University (Finland) is seeking a motivated and talented Doctoral Researcher (PhD Student) to work at the intersection of Probabilistic Machine Learning, Bayesian Inference, and Dynamical Systems (Biology-Informed Machine Learning). The position is situated within a world-class collaborative ecosystem spanning the Computational Systems Biology group, the Finnish Centre for Artificial Intelligence (FCAI), and the ELLIS Institute Finland. The candidate will be jointly supervised by Dr. Julien Martinelli and Associate Professor Harri Lähdesmäki.
Key Information Overview
| Detail | Information |
| Position / Designation | Doctoral Researcher (PhD Student) |
| Host Institution | Aalto University |
| Department | Department of Computer Science |
| Location | Otaniemi Campus, Espoo, Finland |
| Research Area | Probabilistic Machine Learning, Bayesian Inference, Biology-Informed Dynamical Systems |
| Employment Type | Full-time (2-year initial contract + option for 2-year renewal) |
| Starting Salary | €3,168.20 per month + Finnish occupational & social benefits |
| Application Deadline | October 21, 2026, at 23:59 (UTC +3) |
| Application Mode | Online via Aalto Workday portal (single PDF) |
Designation
- Doctoral Researcher / PhD Candidate
Research Area
- Probabilistic Machine Learning & Statistics
- Biology-Informed Gaussian Processes (BioGPs)
- Bayesian Dynamical Models & Ordinary Differential Equations (ODEs)
- Uncertainty Quantification & Foundation Models for Dynamical Systems
- Computational Systems Biology & Biomedical Applications
Location
- Otaniemi Campus, Espoo, Finland (Department of Computer Science, Aalto University)
Eligibility / Qualifications
- Academic Degree: Master’s degree (M.Sc.) in Computer Science, Statistics, Applied Mathematics, Machine Learning, Data Science, or a closely related field (must be completed prior to the start of the contract).
- Technical Skills:
- Strong theoretical foundation in probabilistic machine learning, Bayesian inference, and statistics/applied mathematics.
- Strong programming skills in relevant languages (e.g., Python/PyTorch/JAX/Julia).
- Language Proficiency: Excellent written and oral English communication skills.
- Research Mindset: High motivation, independent problem-solving skills, and a genuine interest in biology-informed/mechanistic machine learning.
Scholarship / Position Description & Benefits
- Contract Duration: 2-year fixed-term appointment with an option for a 2-year renewal (covering the standard 4-year doctoral track).
- Remuneration: Starting salary of €3,168.20 per month, following the Aalto University salary system for doctoral researchers.
- Benefits:
- Comprehensive occupational healthcare services.
- Full access to Finland’s generous social security benefits and public services.
- Annual workload of 1,612 hours combining dedicated research and limited teaching support.
- Academic Environment: Member of FCAI (Finnish Centre for Artificial Intelligence) and ELLIS Institute Finland, with extensive collaborative opportunities across Nordic and European machine learning hubs.
How to Apply
Applications must be submitted electronically through the Aalto University Workday recruitment system.
All application documents must be combined and uploaded as one single PDF file, containing:
- Curriculum Vitae (CV) detailing educational background, skills, and any publications or projects.
- Cover Letter / Motivation Letter detailing your research interests and describing how they align with the project goals and Dr. Martinelli’s research focus.
- Sample of Academic Writing (e.g., Master’s/Bachelor’s thesis, course report, or conference/workshop paper).
- Official Academic Transcripts (both B.Sc. and M.Sc. study records).
- Degree Certificates (latest completed degree).
For inquiries regarding the research project: Contact Dr. Julien Martinelli (julien.martinelli@aalto.fi).
For questions regarding the recruitment process: Contact HR Advisor Susanna Holma (hr-cs@aalto.fi).
Last Date to Apply
- October 21, 2026, at 23:59 (UTC +3)(Note: Applications are reviewed continuously, and the position may be filled as soon as a suitable candidate is identified).
