Doctoral Researcher in Machine Learning for Predictive Maintenance of Maritime Machinery

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 13,000 students, 400 professors and close to 4,500 other faculty and staff working on our dynamic campus 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 Engineering drives science and innovation in industrial and built environment technologies. We are committed to educating a new generation of experts who combine technical excellence with a deep understanding of sustainable development in shaping societies. Our research focuses on sustainable built environment, mechanics and materials, multidisciplinary energy technology, and design and implementation of technical systems. The strength of our school lies in close collaboration with stakeholders across research and education. About 45 doctoral candidates and 350 master’s students graduate from the school every year. The school is home to 700 staff members, including 70 professors. To learn more, please visit eng.aalto.fi.

We are now looking for a

Doctoral Researcher in Machine Learning for Predictive Maintenance of Maritime Machinery

We are looking for a doctoral researcher to join the Research Council of Finland–funded TRANSITION-MODEL project at Aalto University’s School of Engineering, Department of Energy and Mechanical Engineering. The project advances predictive maintenance from offline modelling toward real-time decision support by combining reliability engineering, probabilistic modelling, machine learning and maritime machinery data.

The position is suitable for a highly motivated candidate who wants to build a doctoral profile at the intersection of prognostics and health management, data-driven modelling and maritime engineering. The work will focus on anomaly detection, degradation modelling and remaining useful life prediction for ship machinery using experimental and operational data.

Your role and goals

Your main goal is to conduct doctoral research within TRANSITION-MODEL and contribute to high-quality scientific publications, reproducible methods and doctoral-level training. The work will focus on uncertainty-aware machine learning and probabilistic models for condition monitoring and predictive maintenance of maritime machinery.

Your tasks include:

• Developing models for multivariate time-series anomaly detection, degradation modelling and remaining useful life estimation.
• Working with experimental and operational machinery datasets, including preprocessing, feature representation, uncertainty quantification and model validation.
• Investigating latent-variable, deep-learning and Bayesian approaches for learning informative health-state representations.
• Designing transparent evaluation protocols, including training/test separation, thresholding, false-alarm analysis and robustness assessment.

Your network and team

The doctoral researcher will join a research environment that combines reliability engineering, marine technology, statistical learning and industrial analytics.

You will work with:

• Aalto University’s wider School of Engineering research community, including opportunities for interdisciplinary collaboration across mechanical engineering, marine technology, operations research and data-driven engineering.
• International academic collaborators and, where relevant, external stakeholders connected to ship machinery, experimental testing and predictive-maintenance applications.

Your experience and ambitions

We are looking for a candidate with a strong analytical mindset, clear research motivation and the ability to work independently while contributing constructively to a research team.

We expect you to have:

• A relevant master’s degree, for example in mechanical engineering, marine engineering, reliability engineering, industrial engineering, data science, applied mathematics, computer science, electrical engineering, automation, or a closely related field.
• A solid foundation in at least one of the following areas: machine learning, statistical modelling, time-series analysis, reliability engineering, deep learning, condition monitoring, signal processing, Bayesian inference, or prognostics and health management.
• Programming skills suitable for research implementation, preferably in Python, MATLAB or a similar scientific computing environment.
• Good written and spoken communication skills in English. Finnish language is not required.

The selected candidate must fulfil the admission requirements for doctoral studies at Aalto University School of Engineering. The employment contract can be finalized after the doctoral study right and other recruitment requirements have been confirmed, according to Aalto University procedures.

What we offer

This position offers the opportunity to work on a timely and scientifically ambitious research topic with clear relevance to safety, efficiency and sustainability in maritime and industrial operations. You will contribute to a project that connects rigorous modelling with real machinery data and aims to move predictive maintenance closer to practical, trustworthy and real-time use.

• Supervision, research guidance and sparring to support your progress as an independent researcher.
• Opportunities to publish, present at international conferences and build an academic network.
• Aalto University’s professional development opportunities, doctoral training and interdisciplinary research culture.
• A vibrant Otaniemi campus in Espoo, with excellent public transportation connections and a strong international community.

The position is fixed term. The expected starting date is no later than September 2026. Following the standard practice in the Department of Energy and Mechanical Engineering, the contract will be made initially for two years, with possible extension depending on progress (successful mid-term progress review), funding and the standard practices of the School of Engineering. The work is full-time and the primary workplace is Aalto University’s Otaniemi campus in Espoo, Finland. Hybrid work may be possible according to Aalto University practices and the needs of doctoral research.

The salary is determined according to Aalto University’s salary system and the collective agreement for Finnish universities. The final salary depends on the selected candidate’s qualifications, experience and the stage of doctoral studies.

Join us!

To apply, please submit your application through Aalto University’s recruitment site by 23.59 (EET) on 10.07.2026. Please include the following documents as a single PDF or as separate files according to the recruitment system instructions:

• A motivation letter describing your research interests, relevant background and fit with the position (1 page).
• A curriculum vitae, including publication list if applicable.
• Degree certificates and transcripts of records for bachelor’s and master’s studies.
• Summary of master’s thesis.
• Proof of proficiency in English if the applicant is not a native speaker (see eligibility and language requirements).

Please note: Aalto University’s employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their existing Workday user account, not via the external webpage for open positions. If you are a student or visitor at Aalto University, please apply with your personal email address, not your aalto.fi address, via Aalto University open positions.

For more information about the role, please contact Dr. Ahmad BahooToroody, ahmad.bahoo@aalto.fi. For questions related to the application process, please contact Katariina Pehkonen, firstname.lastname(at)aalto.fi.

Want to know more about us and your future colleagues? You can watch these videos: This is Aalto University; Aalto University – Towards a better world; and Shaping a Sustainable Future.

Read more about working at Aalto: https://www.aalto.fi/en/careers-at-aalto

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About Finland

Finland is a great place for living with or without family – it is a safe, politically stable and well-organized Nordic society. Finland is consistently ranked high in quality of life. For more information about living in Finland, please see Aalto Careers for International Staff.

Espoo, 16.06.2026.

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