Knut and Alice Wallenberg AI Consultancy Grant

Each granted research group may receive access up to 700 hours AI expertise per funding round. This is a long-term initiative, with several calls for applications planned.

The grant was established in response to the rapid development of artificial intelligence. Research and innovation are areas in which the technology is expected to lead to major breakthroughs far more quickly than before—and to discoveries that might otherwise not even be possible.

Researchers with active grants from Knut and Alice Wallenberg Foundation, Marianne and Marcus Wallenberg Foundation, or Marcus and Amalia Wallenberg Foundation are eligible to apply.

Knut and Alice Wallenberg AI Consultancy Grant 2026

Vladislav Orekhov, University of Gothenburg
AI-assisted assignment from super-resolution NMR spectra

Göran Johansson, Chalmers University of Technology
AI-assisted calibration of superconducting quantum processors

Vicent Pelechano, Karolinska Institutet
AI-based feature learning from 5PSeq for AMR phenotyping

Eduardo Villablanca, Karolinska Institutet
AI-driven discovery of spatial host microbiota niches controlling mucosal healing in IBD 

Jens Carlsson, Uppsala University
Domain-adaptive machine learning to overcome chemical domain shift between DNA-encoded libraries and billion-scale commercial chemical space

Igor Zozoulenko, Linköping University
Machine Learning Driven Modelling of Bio- and Conducting Polymers 

Erik Benson, Karolinska Institutet
Mining synthetic DNA selection data for hidden binders or targeting agents 

Johan Zelano, University of Gothenburg
Optimizing epilepsy treatment through population health data 

Leona Achtenhagen, Jönköping University
Profit versus nature? Disentangling the polarized media discourse on forest management in Sweden 

Giovanna Sammarco Tancredi, Chalmers University of Technology
Quantum Gate Optimization with Digital Twins of Superconducting Qubits 

Gonçalo Castelo-Branco, Karolinska Institutet
Spatial AI for Predicting Cell Fate Transitions and Disease Course in Multiple Sclerosis