We often use stochastic descriptions to model complex systems. Many of our projects involve kinetic Monte Carlo, agent-based models and multiscale modeling. A reoccurring theme is how to leverage distributed e-infrastructure for simulations and how to use machine learning to construct approximations.
A core theme in the group is the use of artificial intelligence and machine learning to make software and infrastructure more efficient, interactive and scalable. We also do disciplinary research into specific areas of ML, such as likelihood-free inference and privacy-preserving federated machine learning.
Our research in distributed computing and data engineering ranges from development of new ways to manage large and fast data to design and development of massively parallel, interactive, cloud native applications operating in cloud, fog and edge infrastructure.
The group participates in the eSSENCE strategic initiative on eScience, focusing on eScience tools and technologies.
Scaleout Systems is a spin-off from the group, focusing on privacy-preserving AI and cloud-native machine learning.
Looking for postdocs candidates both in computational systems biology and in data science&engineering, please reach out to me to know more 🙂 #postdoc #systemsbiology #datascience
Postdoc opportunity with Elisabeth Larsson as part of the eSSENCE strategic initiative on eScience: https://uu.se/en/about-uu/join-us/details/?positionId=352683 #eScience
@scaleoutsystem I’m also giving away one of our key strategic advantages of being based in Uppsala 🤦♂️😊
