University
www.en.aau.dk/?utm_source=chatgpt.comCopenhagen, DenmarkAalborg University (AAU) is a leading Danish research university recognised for innovation, interdisciplinarity, and strong industry collaboration. Founded on its internationally renowned Problem-Based Learning (PBL) model, AAU integrates research, education, and societal impact, enabling students and researchers to work directly with real-world challenges.
The Copenhagen campus hosts vibrant research communities in computer science, artificial intelligence, machine learning, software engineering, sustainability, and digital technologies. AAU researchers are active in European and international collaborations, including Horizon Europe, MSCA, ERA-NET, and major national funding programmes.
AAU places strong emphasis on collaborative, applied, and high-impact research. We combine theoretical excellence with practical relevance, often working closely with companies, public institutions, and global academic partners. Our mission is to create knowledge that advances society, supports innovation, and contributes to solving complex scientific and technological challenges.
We welcome international researchers and partners who seek an open, creative, and supportive research environment.
SCIENTIFIC PANELS
Expertise
Expertise in probabilistic machine learning and uncertainty quantification for spatio-temporal forecasting and climate-aware modelling.
Expertise
Reliable Large Language Models Through Ensemble Methods and Wisdom-of-the-Crowds Principles
Expertise in designing reliable LLM systems via ensembles, uncertainty quantification, and wisdom-of-the-crowds principles.
Expertise
Probabilistic Machine Learning for Genome Recovery and Microbial Data
Probabilistic ML, deep generative models, and uncertainty-aware microbial genome recovery and large-scale metagenomics.
Expertise
Probabilistic Machine Learning and Generalization Theory in Modern Deep Learning
Probabilistic ML, PAC-Bayes/Chernoff theory, uncertainty, and generalization analysis for deep learning and over-parameterized models.