MLFF Distillation & GCMC Integration - Internship

The role

You will develop fast, accurate machine learning force fields tailored to high-throughput Monte Carlo simulation. You will integrate these into the in-house simulation framework, kUPS, as an engineering intern within the chemistry team.

What you will do

• Distill MLFFs into fast student potentials optimised for Monte Carlo simulations.
• Curate, version, and document training and validation datasets.
• Run head-to-head validation campaigns comparing distilled MLFF against classical force-field baselines.
• Profile and optimise the pipeline for throughput.
• Benchmark accuracy/speed trade-offs systematically.
• Collaborate with computational chemists on reference data generation and validation strategy.
• Contribute to a publication establishing MLFF-driven GCMC for MOF screening.

Required qualifications

• Currently enrolled in (or recently completed) a PhD or Master's programme in a relevant quantitative field (Physics, Chemistry, Chemical Engineering, Computational Science, Machine Learning, or simila
• Experience in adsorption modelling at the atomic scale.
• Hands-on experience with molecular simulation methods (GCMC, MD, or both).
• Comfortable working on Linux environments and managing simulation campaigns at scale.
• A genuine interest in the application of ML to chemistry and materials science.

Nice to have

• Familiarity with modern MLFFs.
• Experience with knowledge distillation or other model compression techniques for scientific ML.
• Experience with active learning workflows for atomistic data.
• Familiarity with DFT data generation and the practicalities of curating atomistic datasets.
• Direct experience with established simulation packages.
• Background in gas adsorption, MOFs, or porous materials.
• Familiarity with classical force fields used in MOF simulation.
• A track record of published research at top-tier ML or computational chemistry venues.

Benefits

• Competitive salary
• Equity in CuspAI
• 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays
• 26 weeks parental leave for primary caregiver at full pay
• 12 weeks parental leave for secondary caregiver at full pay
• Professional development budget

About the company

CuspAI is a frontier AI company on a mission to solve the breakthrough materials needed to power human progress. The founding team is comprised of researchers in AI, chemistry and engineering.