STFC Scientific Computing is inviting in-field placement applications for a project that aims to develop a set of blueprints for AI for Science applications at Exascale. The placement is supported through an EPSRC[i] funded project called 'Blueprinting AI for Science at Exascale (BASE-II).
We have up to six placements available. The main objective for each placement is to spend time with STFC Scientific Computing's Scientific Machine Learning Group (SciML) at the Rutherford Appleton Laboratory. You will gain ML, data science, and data engineering skills to take back to your organisation or for your own research.
During your placement, SciML will provide coaching on a broad range of machine learning skills, as follows:
- Basic Carpentry Skills covering ML for Science, platforms for AI and computational science, research data engineering/management
- Computational Science and AI: Surrogate models for simulations, generative models for computational sciences, and physics informed neural networks (PINNs)
- Advanced AI for Science: AI patterns for science and engineering, AI benchmarking, latent-space modelling, bridging the gap between experimental and simulated datasets, HPC-AI converged models, AI at the Edge, data denoising, domain-specific ML models, and large-language models (LLMs) for science.
This placement is non-contractual with STFC and as such no transfer of employment will be made to STFC as the host organisation. All placements will be based at STFC's Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX.
We will consider placements for a minimum of 3 months and a maximum of 6 months. In each case, successful applicants will be regarded as visiting scientists at STFC, with access to STFC systems to conduct day to day work within the SciML group.
Essential Skills you will need for this placement:
- Currently working within the discipline of Data Science / Engineering role
- Good programming skills on Python and Linux
- Strong technical background in computer science or electrical engineering or a relevant area, e.g., mathematics, materials sciences, physics, chemistry or life sciences or computationally driven areas of sciences (including environmental sciences) or equivalent experience in other areas.
If this placement sounds right for you, please submit your expression of interest form
DEADLINE: 5pm, Thursday 30th November 2023
[i] Engineering and Physical Sciences Research Council