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 theme in the group is the use of artificial intelligence and machine learning to make software and infrastructure intelligent, interactive and scalable. We also do disciplinary research into specific areas of ML, such as likelihood-free inference and federated ML.
Our research on e-Infrastructure and data engineering ranges from development of new ways to manage large and fast data to massively parallel, interactive and cloud native applications, to management and scalability of cloud 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.
Two postdoctoral positions available for modeling of stochastic gene expression in mammalian cells http://grimagroup.bio.ed.ac.uk/vacancies
Come work with us @UU_University! We are recruiting an associate professor in Scientific Computing with specialization in Data Science. Help us shape AI, Data Science & Engineering efforts at the department! https://uu.se/en/about-uu/join-us/details/?positionId=298728
Work with Tom Prescott and me, to appear in SIAM / ASA J. UQ! https://twitter.com/OxUniMaths/status/1193862084431405060
Our article "On-demand virtual research environments using microservices" has been published today in @PeerJCompSci https://peerj.com/articles/cs-232 #Bioinformatics #ComputationalBiology #SoftwareEngineering
