AI for Science
AI can transform facilities science by making links across different data sources, bringing guidance and automation to experiment analysis, making expert decisions during experiments, and lowering the barrier to accessing complex experimental and simulation techniques.
Current activities explore areas such as:
- Detecting optical damage and triggering preventative measures.
- Inspecting, sorting and classifying regions and particles within electron microscopy to improve automation.
- Developing surrogate models for complex modelling tools with the Computational Materials and Molecular modelling theme.
- Optimising the alignment of Adaptive Optics for Hyperspectral Imaging via Synchrotron InfraRed.
- Using Surrogate Models to assist with the optimisation of a particle accelerator.
This activity is underpinned by core expertise and development in five key areas:
- AI for Data Analysis: Equipping the scientific community with state-of-the-art data analysis techniques to maximize insights from large-scale experimental datasets produced by STFC facilities. Our tools and methodologies are designed to handle complex data challenges and drive ground breaking discoveries.
- AI for Science Benchmarks: Creating and refining benchmarks to evaluate the performance and effectiveness of various AI models across different systems in scientific applications. Our objective is to promote openness and advance scientific research by providing transparent and rigorous evaluation metrics.
- AI for Scientific Image Processing: Applying advanced AI techniques to enhance image quality and identify objects within scientific images.
- AI for Smart Facilities: Developing cutting-edge instrumentation and control algorithms to ensure STFC facilities are advanced, automated, and intelligent.
- Core AI Research: Addressing fundamental challenges in AI that impact STFC-funded facilities and programs.
FAQs
What is the Ada Lovelace Centre?
The Ada Lovelace Centre is a collaborative hub that helps UK science facilities use computing to solve complex research challenges. It brings together experts in data, software, and artificial intelligence to accelerate scientific progress.
What do we offer?
The Ada Lovelace Centre will fund 50% of the cost of a studentship (fees and stipend) with a partner University.
What are the terms of the funding?
The student is expected to spend a significant portion of their time (30-50%) with Ada Lovelace Centre teams and working with facilities during their PhD. In practice, this is typically arranged as a block of time, typically in year 2, spent with the Ada Lovelace Centre supervisors.
The aim of the studentships is to support forward looking developments and to build both the links between facilities and Ada Lovelace Centre and the wider expertise which supports our goals. This therefore requires that the PhD supervision is not simply between a University and a facility but links strongly to the scientific computing expertise in the Ada Lovelace Centre.
What areas of expertise does the Ada Lovelace Centre cover?
The Ada Lovelace Centre supports a wide range of scientific computing themes, including AI for science, computational biology, mathematical modelling, data engineering, and infrastructure development.
How long can the studentships be?
Studentships can be for a maximum of 4 years.
When is the call?
The call for proposals typically launches in June each year with a closing date of mid-late September. Successful proposals will be informed in October.
The call includes more detailed terms and guidance.
