PhysicsX builds physics-based AI for industrial engineering that predicts physical behaviors quickly to accelerate design testing across industries.
<div class="content-intro"><h2>About us</h2> <div>PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.</div> <div>We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.</div></div><h2><strong>Who We’re Looking For</strong></h2> <div> <p>You are a problem solver and builder, passionate about creating practical solutions that help customers make better engineering decisions. You can grasp and apply advanced engineering concepts across multiple industries, and you excel at working directly with internal and external stakeholders, often on-site, to develop high-fidelity simulation models that feed into AI tools that are both useful and used.</p> <p>You bring deep expertise in fluid mechanics, heat transfer, and multiphase modelling within bioprocess and chemical engineering environments. You are highly proficient in at least one of Star-CCM+, OpenFOAM, or Fluent, and experienced modelling the complex flow behaviour that arises in industrial bioprocess systems. You are adept at automating these tools to create scalable optimisation workflows. Experience in parametric CAD modelling (NX or CATIA) and coding in Python (or the ability to pick up new programming languages quickly) is an advantage.</p> <p>With 5–7 years of industry experience (post-MEng, MSc, or PhD) in a commercial environment, you are ready to hit the ground running. You are confident setting up simulations independently, interpreting complex results with rigour, and making sound decisions grounded in solid engineering judgement.</p> </div> <h2>What you'll do</h2> <ul> <li>Proficiency in CFD solvers across open-source OpenFOAM and commercial platforms as a plus (Star-CCM+, Fluent or equivalent), including custom solver and boundary condition development.</li> <li>Develop multiphase flow models for gas-liquid, solid-liquid, VOF, Euler-Euler/Euler-Lagrange approaches) across a range of industrial and process engineering applications.</li> <li>Model non-Newtonian and complex fluid rheology modelling, including high-viscosity, shear-thinning, or particle-laden flow regimes</li> <li>Reactive and coupled-physics flow modelling — link CFD with reaction kinetics, heat transfer, or domain-specific process models (e.g. biokinetic, combustion, or electrochemical frameworks)</li> <li>Own meshing generation and strategy for complex industrial geometries, including rotating machinery, internal flow passages, spargers, and free-surface interfaces</li> <li>Build robust parametric CAD models (NX, CATIA, or equivalent) tightly coupled with simulation pipelines, enabling automated design optimisation and DoE studies</li> <li>Multi-physics model development end-to-end: geometry clean-up, meshing, solver setup, post-processing, and experimental data integration for model validation</li> <li>Scale-up/scale-down methodology — translate small-scale experimental data to full-scale CFD models and iterating model fidelity against physical measurements</li> <li>HPC experience: job scheduling, MPI-based distributed computing, GPU acceleration, and performance tuning for large-mesh transient simulations on cloud (Flux) and on-premise resources</li> <li>Surrogate modelling and ML-CFD coupling — build reduced-order or AI surrogate models from high-fidelity CFD data to support design space exploration and process optimisation</li> <li>Data pipeline literacy — structuring and curate CFD output datasets for downstream AI/ML training, active learning, and Pareto optimisation workflows; applying data sampling techniques (LHS, quasi-random, adaptive) to efficiently cover design space</li> <li>Experimental validation workflows — compare simulation predictions against physical test data, interpreting discrepancies, and systematically improving model fidelity</li> <li>Customer-facing delivery — partner with clients to scope and address complex engineering challenges via CAE and AI solutions; communicating results clearly, recommending actionable next steps, and balancing accuracy with efficiency under commercial deadlines</li> </ul> <h4>Engineering and workflow</h4> <ul> <li>5–7 years post-graduate experience </li> <li>MEng, MSc, or PhD in mechanical, chemical, or process engineering</li> <li>Python scripting and simulation automation</li> <li>DoE, surrogate modelling, and design space exploration</li> <li>Parametric CAD modelling (NX or CATIA advantageous)</li> <li>Strong written and verbal communication with technical and non-technical audiences</li> </ul> <p> </p> <h2><strong>What we offer</strong></h2> <p><strong>Build what actually matters</strong></p> <p>Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.</p> <p><strong>Learn alongside exceptional people</strong></p> <p>Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.</p> <p><strong>Influence over hierarchy</strong></p> <p>We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status