Overview
Aalborg University is seeking applications for a postdoctoral position focused on probabilistic machine learning with an emphasis on spatio-temporal data modeling. This role is situated within the Department of Computer Science at the Copenhagen Campus and is part of the DK-Future project, which aims to enhance predictive models for land use in Denmark considering climate impacts. This opportunity is significant for researchers interested in applying advanced machine learning techniques to real-world environmental challenges.
Background & Relevance
The field of probabilistic machine learning is crucial for understanding and predicting complex systems, particularly in the context of climate change and land use. Spatio-temporal modeling allows researchers to capture the dynamics of environmental data over time and space, making it essential for effective decision-making in sustainability and urban planning. This postdoctoral position aligns with the growing need for innovative approaches to tackle climate-related uncertainties.
Key Details
- Position Start Date: 1 January 2026 (or soon thereafter)
- Duration: 18 months, with potential for extension based on performance
- Location: Copenhagen, Denmark
- Application Deadline: 21 November 2025
- Employment Type: Full time
- Reference Number: 2025/631
- Project: DK-Future – Probabilistic Geospatial Machine Learning for Predicting Future Danish Land Use under Compound Climate Impacts
- Funding: Villum Foundation (Synergy Programme)
- Application Link: Aalborg University Vacancies
Eligibility & Participation
This position is targeted at individuals who have completed a PhD in Machine Learning, AI, Computer Science, Statistics, or a related discipline. Candidates should possess experience in probabilistic or Bayesian methods, variational inference, spatio-temporal modeling, or deep learning frameworks such as TensorFlow, PyTorch, or JAX. Familiarity with geospatial or environmental data is advantageous but not mandatory.
Submission or Application Guidelines
Interested candidates should prepare their applications according to the guidelines provided on the Aalborg University vacancies page. Ensure that all required documents are submitted before the deadline of 21 November 2025. For any inquiries, applicants can contact Assoc. Prof. Andrés R. Masegosa at arma@cs.aau.dk.
Additional Context / Real-World Relevance
The integration of probabilistic methods in machine learning is increasingly relevant as the world faces complex environmental challenges. By developing models that can predict land-use changes under varying climate scenarios, researchers can provide valuable insights that inform policy and planning. This position not only contributes to academic research but also has the potential to impact real-world applications in sustainability and environmental management.
Conclusion
This postdoctoral position at Aalborg University presents an exciting opportunity for researchers passionate about machine learning and its applications in environmental science. Interested individuals are encouraged to apply and contribute to this important field of study, advancing knowledge and solutions for future land use in Denmark.
Category: PhD & Postdoc Positions
Tags: machine learning, probabilistic methods, spatio-temporal modelling, bayesian, deep learning, copenhagen, aalborg university, geospatial data
