NeurAtom: Founding Machine Learning Engineer — Surrogate Modelling for Reactor Physics

Job Description

ABOUT THE COMPANY

NeurAtom develops neural-network surrogate models for reactor-physics simulation. Full Monte Carlo neutron-transport calculations — the reference method for reactor core design and fuel optimisation — take hours on high-performance computing clusters. Our surrogates reproduce the quantities of interest (spatial power distribution, k-eff, reactivity) at sub-millisecond evaluation, validated against Monte Carlo ground truth. This makes design-space exploration and fuel-loading optimisation, currently constrained by simulation cost, tractable as interactive problems.

The context: a substantial expansion of nuclear construction is underway across Europe, with a large number of SMR and Gen-IV designs entering the design phase. Each new core requires extensive neutronics analysis. The computational methods in current use are either high-fidelity and slow (Serpent, OpenMC, MCNP) or fast and approximate; our work aims to reduce that trade-off. NeurAtom is a deep-tech spin-out from KTH, currently in the KTH Innovation Launch Programme.

ACCOMPLISHMENTS

NeurAtom is at an early stage. Current status, stated plainly:

• Two integrated prototypes.
• Validation against a trusted reference. Surrogates validated against Serpent 2 Monte Carlo (JEFF-3.1.1) on the SUNRISE-LFR lead-cooled fast-reactor geometry, using approximately 120,000 simulations on the Dardel national supercomputer.
• Known limits. All validation to date is on a single reactor geometry in an academic setting. The methodology has not yet been applied to industrial proprietary data — closing that gap is the substance of this role.
• Institutional standing. Accepted into the KTH Innovation Launch Programme (Batch 23), with access to premises, coaching, network, and proof-of-concept funding.

THE ROLE

The methodology performs well on the geometry it was developed on. The central objective is to make it transfer reliably to reactor designs and datasets it has not seen, and to develop it into a maintainable product. This position owns the surrogate-modelling methodology end to end. Principal areas of responsibility:

• Training-data strategy and adaptive sampling. Design how the input design space is sampled, and build the active-learning loop that selects which simulations to run next. Monte Carlo ground truth is expensive; this determines the cost of onboarding each new reactor geometry and is the primary lever on whether a reactor-agnostic product is economically viable.
• Data conditioning and validation. Distribution diagnostics, handling of heavy-tailed targets, normalisation choices and their failure modes, and stratified and adversarial validation.
• Validation envelope and uncertainty quantification. Characterise where predictions are reliable and where they are not, including out-of-distribution behaviour, and attach calibrated uncertainty to predictions. In a safety-relevant, licensing-adjacent context this is a requirement, not an enhancement.
• Optimisation over surrogates. Own the optimiser that searches loading patterns, including the interaction between surrogate error and an optimiser that will exploit regions where the surrogate is least accurate.
• Architecture selection and benchmarking. Benchmark architectures where the differences are consequential — sparse, high-gradient regions of the design space — without over-investing where models converge to similar performance.
• Reproducibility. Experiment tracking, model and dataset versioning, and regression testing, so that the validation record is auditable rather than reconstructed from memory.

You would be the sole owner of this workstream, working directly with the founders. There is no ML team above you and no established process to inherit.