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PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software.
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.
Overview:
We are seeking a highly motivated Computational Fluid Dynamics Intern for a full time, 3-month program focused on advanced CFD workflows using OpenFOAM. This opportunity is open to final-year students as well as recent graduates (within one year) holding a Master’s or PhD in a relevant discipline. You will collaborate with experienced researchers and engineers to develop, execute, and validate high-fidelity simulations, gaining hands-on exposure to real-world fluid dynamics challenges and HPC environments.
Please note that we are unable to offer sponsorship for this position. As such, applicants must have full eligibility to work in the UK. This includes being a UK citizen or, if not a UK citizen, having graduated or being in the process of graduating from a UK university.
What you will do
- Train and master OpenFOAM: Dive deep into its finite-volume implementation, solver structure, and dictionary configurations through structured tutorials and code walkthroughs.
- Preprocess geometries and generate meshes: Use open-source tools to create high-quality, adaptive meshes tailored to complex CAD models.
- Set up and run CFD cases: Configure boundary and initial conditions, select appropriate solvers, and launch simulations on local workstations or HPC/cloud clusters.
- Automate workflows: Develop Python or C++ scripts to streamline case setup, job submission, and post-processing routines—learn about automated workflows end-to-end.
- Integrate data-driven methods: Explore and apply machine learning technologies for sim optimization.
- Post-process results: Use ParaView or similar tools to extract performance metrics, generate contour plots, and create animations for sharing insights.
- Validate and optimize models: Compare simulation outputs against experimental data or literature benchmarks, refine mesh and solver settings to improve accuracy.
- Document and present findings: Prepare clear technical reports and deliver presentations to the project team, highlighting key results and recommendations.
What you bring to the table
- Academic background: Final-year Master’s or PhD students, or recent graduates (within one year) in Mechanical Engineering, Aerospace Engineering, Applied Physics, or a related discipline with a strong focus on fluid mechanics and numerical methods.
- OpenFOAM familiarity: Demonstrated experience in using solvers, and troubleshooting case failures. Strong grasp of solver selection, turbulence modelling, convergence criteria, mesh quality metrics, and post-processing workflows.
- Meshing skills: Hands-on experience with both open-source and/or commercial meshing tools, including mesh refinement and adaptive techniques.
- Programming ability: Comfortable with Python or C++ for scripting batch runs, data extraction, and simple solver modifications.
- HPC/cloud familiarity: Knowledge of job schedulers (e.g., SLURM) and deploying simulations on clusters or cloud platforms.
- Soft skills: Strong analytical thinking, attention to detail, and clear communication to work effectively within multidisciplinary teams.
- Enthusiasm to learn: Eager to explore advanced CFD techniques, automated workflows, and machine-learning-aided simulations.
What you'll learn as part of the team
- Hands-on CFD expertise: Master the end-to-end OpenFOAM workflow—from mesh generation to solver customization and post-processing.
- Automated workflows: Build and deploy scripts to fully automate simulation pipelines, from CAD import through results extraction.
- Machine learning technologies: Gain exposure to ML tools and frameworks applied to CFD.
- High-performance computing: Practical experience running large-scale simulations on HPC clusters and leveraging parallel computing resources.
- Professional mentorship: Work alongside senior CFD engineers, receive feedback on your modeling approach, and build a network in a cutting-edge R&D environment.
- Real-world impact: Contribute directly to projects with tangible outcomes, such as validated models for energy storage, aerospace, or industrial flow systems.
This internship offers a focused, immersive experience in CFD, tailored for those with a familiarity with OpenFOAM, advanced academic credentials, and a passion for automation and data-driven methods. You’ll emerge with both the technical prowess and professional acumen to excel in computational fluid dynamics roles.
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