Departments / Extreme-Scale, Nano & Fundamental Engineering
Research and engineering organised across a conceptual span from 10^-100 m to 10^100 m — from ultra-small theoretical and mathematical regimes through quantum, atomic, molecular, nano, micro, human, planetary and cosmological scales. The span is a research classification, not a claim of experimental reach: every opportunity states whether the work is theoretical, mathematical, computational, simulation, experimental, prototype, applied engineering, or long-horizon frontier research.
From ultra-small theoretical and mathematical regimes through quantum, atomic, molecular, nano, micro, human, planetary and cosmological scales. Scale bands are a research classification, not a claim of experimental reach. Most of this span cannot be probed by any apparatus that exists: nothing below about 10^-19 m has been measured directly, and anything at 10^26 m or beyond is inferred from observation rather than engineered. Every opportunity states the kind of work it actually is.
Every posting in this department carries one of these classifications, and states its evidential standing separately. Established science, active experimental research, computational research, theoretical research and speculative frontier research are kept visibly distinct.
Theoretical
Formulating and analysing physical theories, and deriving what they predict.
Mathematical
Proving results and building the mathematical structures other work is expressed in.
Computational
Computing quantities that cannot be obtained in closed form, from established physics.
Simulation
Building and running simulations of systems too large or too strongly coupled to solve directly.
Experimental
Measuring real systems with real apparatus, and analysing what the apparatus produced.
Prototype
Building a working article to find out what the design does outside the model.
Applied engineering
Engineering to a specification, on established science, for something that has to work.
Long-horizon frontier research
Open questions with no current experimental test, pursued for what the attempt itself yields.
Each division states its scale range and the kind of work it does at the top of its own page, along with the honest limits of that work.
10^-100 m to 10^-35 m
Mathematical and theoretical work on the structure of spacetime, quantum foundations and the frameworks that attempt to describe physics below the Planck length. Entirely theory, mathematics and computation.
10^-18 m to 10^-12 m
Modelling and data analysis for particle, high-energy and nuclear physics: interaction models, Monte Carlo pipelines, detector data, and radiation effects in materials.
10^-12 m to 10^-8 m
Quantum information, algorithms, device modelling, sensing and atomic simulation, in the band where quantum states are measured directly rather than inferred.
10^-10 m to 10^-7 m
Quantum chemistry, molecular dynamics and molecular design: computing what a molecule does, and comparing the prediction with measurement wherever measurement exists.
10^-9 m to 10^-6 m
Nanomaterials, nanostructures, nanoelectronics, nanophotonics, sensing and metrology, across the band where structures are fabricated, imaged and characterised routinely.
10^-10 m to 10^0 m
Materials discovery and modelling across alloys, ceramics, polymers, composites, metamaterials, quantum and energy materials, and materials for extreme environments.
10^-6 m to 10^-3 m
Device physics, microelectronics and VLSI, MEMS and NEMS, sensors and photonics, in the band where design, simulation and prototype meet.
10^-100 m to 10^100 m
The cross-department division: research software, simulation platforms, HPC, AI for science, digital twins, data pipelines and the reproducibility every other division depends on.
10^-100 m to 10^100 m
The mathematical foundations the whole department stands on: numerical methods, convergence and stability, optimisation, probability, and the modelling support every other division draws on.
10^-3 m to 10^3 m
Computational mechanics, robotics, precision engineering and advanced manufacturing, and the multiscale modelling that carries a material property up into a system that has to work.
10^0 m to 10^6 m
Structural engineering, smart infrastructure, advanced construction materials, infrastructure sensing and digital twins, and climate-resilient large-scale systems.
10^-9 m to 10^-3 m
Nanobiotechnology, biomaterials, biosensors, computational biology and bioinformatics, tissue engineering, lab-on-chip and microfluidics, and the interfaces between biology and engineered surfaces.
10^-9 m to 10^6 m
Battery and energy-storage materials, hydrogen systems, solar materials, catalysis, carbon capture, water purification, environmental nanotechnology and climate systems modelling.
10^3 m to 10^13 m
Aerospace and space systems engineering, extreme-environment materials, orbital and planetary systems modelling, space robotics and the architecture work behind long-horizon space research.
10^13 m to 10^100 m
Computational astrophysics and cosmology: observational data analysis, gravitational and structure modelling, large-scale simulation, and the mathematics of models at and beyond the observable horizon.
Computer Science, AI & Scientific Computing
What this division builds for the others, who builds it, and the rule that no model ships without its baseline.
Scale 10^-100 m to 10^100 m
Fluent in Python, and able to write code somebody else can read, test and extend. · Data structures and algorithms to the level of choosing correctly and justifying the choice. · Comfortable on Linux, and with Git as a working tool rather than a save button. · Has written tests for numerical code and can explain what a tolerance in a test means. + 6 more
Nano Engineering & Nanotechnology
What this division designs, who designs it, and the rule that maturity is stated in every claim.
Scale 10^-9 m to 10^-6 m
Can explain why a property changes when a material is made small, in terms of surface-to-volume ratio or quantum confinement. · Has interpreted characterisation data — microscopy, diffraction or spectroscopy — and can state what the technique cannot tell you. · Materials science to the level of relating structure to a measurable property. · Can carry out and document a calculation or an analysis in Python that somebody else can rerun. + 4 more
Aerospace, Space & Extreme Environment Engineering
Aerospace systems analysed and designed against requirements with traceable margins.
Scale 10^3 m to 10^13 m
Orbital mechanics: can propagate a two-body orbit and explain what perturbs it. · Has run an engineering analysis for a system with mass, power and thermal constraints that trade against each other. · Understands the space environment: vacuum, radiation, thermal cycling, and what each does to hardware. · Python for mission and systems analysis. + 4 more
Astrophysics, Cosmology & Extreme-Scale Modelling
The pipelines survey data flows through, and the provenance that lets a figure be traced to a run.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 7 more
Astrophysics, Cosmology & Extreme-Scale Modelling
One astrophysical question answered from public survey data, selection function included.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Astrophysics, Cosmology & Extreme-Scale Modelling
Astrophysical systems modelled and compared against observation, at scales that are observed.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Astrophysics, Cosmology & Extreme-Scale Modelling
A small simulation run at two resolutions, with the convergence difference reported.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Astrophysics, Cosmology & Extreme-Scale Modelling
Cosmological simulation and inference, with convergence and systematics treated as first-class.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Astrophysics, Cosmology & Extreme-Scale Modelling
What this division works on, who works on it, and the rule that beyond the observable horizon we describe a model, not the universe.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Astrophysics, Cosmology & Extreme-Scale Modelling
A cosmological calculation carried out and compared against published constraints.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Astrophysics, Cosmology & Extreme-Scale Modelling
Cosmological models and their confrontation with data, including where the model is under-determined.
Scale 10^13 m to 10^100 m
Can explain what a redshift measures and what has to be assumed to turn it into a distance. · Has analysed a real astronomical dataset, including its selection function. · Statistics and Bayesian inference: priors, posteriors, and what a credible interval claims. · Python for astronomical data analysis, in a pipeline that reruns. + 5 more
Aerospace, Space & Extreme Environment Engineering
How one material behaves under an extreme-environment exposure, and what the data does not cover.
Scale 10^-9 m to 10^0 m
Orbital mechanics: can propagate a two-body orbit and explain what perturbs it. · Has run an engineering analysis for a system with mass, power and thermal constraints that trade against each other. · Understands the space environment: vacuum, radiation, thermal cycling, and what each does to hardware. · Python for mission and systems analysis. + 4 more