- Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
- Ability to scope and effectively deliver projects.
- Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
- Excellent collaboration and communication skills — with teams and customers alike.
- PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
1. operator learning (neural operators), or other probabilistic methods for PDEs;
2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
- Ideally, >2 years of experience in a data-driven role, with exposure to:
- building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
- developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
- iterating on network architectures and model structure, tuning and optimizing for inductive biases, improved generalisability, and improved performance;
- combining theoretical reasoning with empirical intuition to guide investigation;
- formulating and running experiment pipelines to benchmark models and produce comparable results;
- writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience.
- Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.