Bernd ReschGeosocial Artificial Intelligence
The growing influence of digital data and tools, along with recent advancements in AI, is transforming how we approach research. With the increasing availability of large, open datasets—ranging from social media posts and news articles to physiological measurements and mobile phone records—entirely new fields of study are emerging. Many of these datasets include geospatial references, offering unique insights into spatial structures across a variety of real-world contexts, such as disaster management, urban well-being, mobility planning, and democratic-political developments.
Our research group is pioneering a dual approach that integrates both “sensing” and “actuating” processes. This methodology not only focuses on analysing social environments but also actively shaping them through data-driven insights. We aim to create novel AI methodologies that provide deeper understanding of geospatial and social processes by analysing human-generated data. This holistic approach to research transcends traditional disciplinary boundaries and integrates concepts from various fields like geoinformatics, computer science, sociology, psychology, education research, and others.
Candidates will join a multi-disciplinary group of researchers with an open mindset for cutting-edge GeoAI research at the intersection of various disciplines. We are seeking machine learning researchers with strong expertise in algorithmic development and the ability to work with complex, multimodal datasets. Candidates will focus on advancing geospatially explicit machine learning techniques, as well as applying generative AI to leverage the multimodal nature of datasets, such as text, location, time or image modalities. Key research areas will include:
- Multimodal Machine Learning: Investigating methods for analysing and integrating heterogeneous data sources simultaneously considering multiple modalities.
- Information Fusion: Creating novel algorithms for combining various data streams into a single information layer.
- Generative AI and Active Learning: Exploring generative models to create synthetic data, involving a human-in-the-loop approach.
- Contextual Enrichment: Designing methods to contextualise information layers within relevant spatial, temporal, semantic or emotional contexts, thus increasing their interpretability.
Researchers in the Geo-social AI Lab work with researchers from various fields, drawing research challenges from real-world problems in multi-disciplinary and international teams. Apart from purely academic collaborations, we are closely working with NGOs, city governments, public authorities, and science communication institutions including the Ars Electronica Center (AEC). As such, researchers contribute to the wider context of the group including the establishment of new cooperations through highly innovative funding proposals.
Candidates are required to hold a degree in artificial intelligence, data science, geoinformatics, computer science, information management or a related discipline. Moreover, an excellent research and publication record, and the ability to work independently and in teams are mandatory.
