Doctoral Researcher in Machine Learning for Predictive Maintenance of Maritime Machinery

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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

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