Current SNIC AI/ML Projects

The area is based on the three-digit level (forskningsämnesgrupp) from Standard för svensk indelning av forskningsämnen applied to the primary classification.

Computer and Information Sciences (9)

AI for improved decision making in healthcare: Fredrik Johansson, Chalmers tekniska högskola
2020-09-01 – 2021-09-01
Deep Learning for Robot Vision: Michael Felsberg, Linköpings universitet
2020-09-04 – 2021-10-01
Deep learning models for modelling genetic variation: Carl Nettelblad, Uppsala universitet
2020-09-04 – 2021-09-01
Efficient communication with Deep reinforcement learning: Emil Carlsson, Chalmers tekniska högskola
2020-09-04 – 2021-09-01
Generative models for reinforcement learning: Emilio Jorge, Chalmers tekniska högskola
2020-09-04 – 2021-09-01
ML for Alzheimer: Lena Stempfle, Chalmers tekniska högskola
2020-09-15 – 2021-10-01
Machine learning for improved decision making: Newton Mwai Kinyanjui, Chalmers tekniska högskola
2020-09-04 – 2021-09-01
Real-time end-to-end Federated Learning: Hongyi Zhang, Chalmers tekniska högskola
2020-09-01 – 2021-09-01
Transferable concepts: Adam Breitholtz, Chalmers tekniska högskola
2020-09-01 – 2021-09-01

Machine learning for topological codes and topological matter: Mats Granath, Göteborgs universitet
2020-08-28 – 2021-09-01

Deep learning for rigorous SW testing and verification: Dhasarathy Parthasarathy, Chalmers tekniska högskola
2020-08-27 – 2021-09-01
Motions and voice: Andreas Jakobsson, Lunds universitet
2020-09-18 – 2021-10-01

Deep learning, Machine learning, and Bayesian computational in epidemiology: Rani Basna, Göteborgs universitet
2020-09-22 – 2021-10-01

Deep Autoencoder Neural Network for identification of Doxorubicin-induced gene expression profile in stem cell-based cardiomyocytes: Dario Melguizo Sanchis, Högskolan i Skövde
2020-09-14 – 2021-10-01