Simulation Engineering Intern - Aeroacoustics CFD

About us

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.

Please only apply to one internship position you feel most aligned with.

If we think you are better suited for another, we will communicate this to you.

Our internship opportunities are designed for students who want to apply what they’ve learned in their degrees to real engineering problems. You’ll work alongside simulation engineers, data scientists, machine learning engineers and research scientists on our projects, gaining hands-on experience and insight into how physical AI is developed and used in industry.

Who We're Looking For

As an Aeroacoustics Simulation Engineering Intern, you will join us over summer 2026 to develop advanced computational frameworks and generate high-fidelity datasets for aeroacoustics analysis and AI modeling applications. This exciting opportunity will allow you to establish end-to-end workflows for predicting and analyzing noise generation and propagation across diverse applications—from external aeroacoustics (vehicle and aircraft noise) to internal cooling flows (HVAC systems, electronics cooling) and emerging technologies like drones and eVTOL aircraft.

Your work will be instrumental in positioning PhysicsX at the forefront of aeroacoustics prediction capabilities, serving rapidly growing markets where noise mitigation is becoming both a regulatory requirement and competitive advantage. You will gain hands-on experience working at the intersection of computational aeroacoustics, high-performance computing, and machine learning, while contributing to cutting-edge research that advances the field of noise prediction and mitigation. Join us in this dynamic role, where your expertise will help establish critical capabilities and push the boundaries of innovation in computational aeroacoustics!

What you will do

The intern will engage in the following activities and deliver key milestones throughout the internship:

• Set up and configure CFD software (e.g., StarCCM) and aeroacoustics solvers (e.g., MGLET) on company HPC systems for production-scale simulations

• Benchmark and evaluate solver pipelines to establish optimal workflows for end-to-end aeroacoustics simulations across multiple application domains

• Conduct comprehensive market value analysis of aeroacoustics expertise and identify target markets (e.g., automotive, eVTOL, data centers) to develop solutions for

• Design and execute simulation campaigns across selected applications, generating high-quality datasets for analysis and modeling

• Implement automated post-processing routines to extract key aeroacoustic metrics including sound pressure levels, frequency spectra, directivity patterns, and noise propagation characteristics

• Assemble and curate simulation results into structured datasets ready for AI surrogate modeling applications

• Collaborate with PhysicsX data scientists and machine learning engineers to structure simulation outputs for training datasets, understand data quality requirements, and participate in model validation workflows

• Prepare detailed technical reports and presentations communicating findings, methodologies, and recommendations

• Develop comprehensive technical documentation explaining workflows, aeroacoustics modeling approaches, underlying physics being simulated, and references to literature and industry standards

• If scope permits, experiment with training AI-based surrogate models for rapid aeroacoustics noise prediction

What You Bring To The Table

Required:

• Currently pursuing a PhD or Masters degree in Mechanical Engineering, Aerospace Engineering, Acoustics, or related engineering field

• Demonstrated experience in computational aeroacoustics (CAA) methods and applications

• Understanding of multi-physics simulation approaches and noise generation mechanisms

• Proficiency in Python or another scripting language for automation, data processing, and workflow orchestration

• Strong analytical skills with ability to evaluate complex simulation results

• Excellent written and verbal communication skills

• Ability to work independently and collaboratively in a fast-paced research environment

Preferred:

• Familiarity with CFD software and aeroacoustics solvers; experience with StarCCM, MGLET, or similar tools

• Experience with High-Performance Computing (HPC) environments, job scheduling systems, and Unix/Linux operating systems

• Experience with experimental design and testing of aeroacoustics

• Experience with AI-driven modeling techniques or surrogate modeling

• Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, PyTorch, MLFlow)

• Background in parametric design automation or mesh generation

• Version control experience and collaborative software development practices

• Interest in machine learning applications for engineering (no prior ML experience required)

Salary:

The estimated range for this position is $45 - 55 USD per hour. The total compensation will be determined by each individual's relevant qualifications, skills and work experience.

Applications will close 10 February 2026.

We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.

We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.