CFD Multiphase & DEM Engineer

PhysicsX; Singapore; Hybrid; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

Shoreditch office

Job summary

PhysicsX, a physics AI company, seeks a CFD Engineer to develop high-fidelity multiphase flow models and automate simulation workflows for industrial applications. You will work with OpenFOAM and Star-CCM+/Fluent, prototype AI-assisted design tools, and deliver validated results for client projects.

Hybrid role with on-site collaboration in our global hubs (London, New York, Singapore) and remote work options. Experience in HPC, Python, and CAD-integrated pipelines is highly valued.

Qualifications

• Proficiency in CFD solvers across OpenFOAM and commercial platforms (Star-CCM+, Fluent or equivalent)
• Develop multiphase flow models for gas-liquid and other regimes across industrial processes
• Model non-Newtonian and complex fluid rheology including high-viscosity and shear-thinning
• Reactive and coupled-physics flow modelling linking CFD with kinetics and heat transfer
• Own meshing strategy for complex geometries and CAD integration with simulations
• Build parametric CAD models tightly coupled with simulation pipelines
• End-to-end multi-physics model development from geometry to validation
• Scale-up/scale-down methods translating lab data to full-scale CFD
• Experience with HPC: MPI, GPUs, cloud/on-premise performance tuning
• Surrogate modelling and ML-CFD coupling for design space exploration
• Data pipeline literacy for CFD outputs and AI training workflows
• Experimental validation workflows comparing with real data
• Customer-facing delivery and actionable recommendations

Responsibilities

• Proficiency in CFD solvers across OpenFOAM and commercial platforms
• Develop multiphase flow models for gas-liquid and other regimes
• Model non-Newtonian and complex fluid rheology modelling
• Reactive and coupled-physics flow modelling — link CFD with kinetics and heat transfer
• Own meshing generation for complex geometries and automated design optimisation
• Build robust parametric CAD models tightly coupled with simulation pipelines
• Multi-physics model development end-to-end: geometry, meshing, solver setup, post-processing
• Scale-up/scale-down methodologies translating small data to full-scale models
• HPC experience: MPI-based distributed computing and GPU acceleration
• Surrogate modelling and ML-CFD coupling for design exploration
• Data pipeline literacy for CFD outputs and AI training workflows
• Experimental validation workflows and updating models based on data
• Customer-facing delivery — scope projects and communicate results

Skills

CFD

OpenFOAM

Star-CCM+

Fluent

Multiphase modelling

Python

NX

CATIA

Automation

Boundary conditions

Tools

MPI

GPU acceleration

OpenFOAM tooling

Job description

About us

Re-architecting Engineering for the Age of Intelligence

PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.

Who We’re Looking For

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.

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.

What you'll do

• 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.
• Develop multiphase flow models for gas-liquid, solid-liquid, VOF, Euler-Euler/Euler-Lagrange approaches) across a range of industrial and process engineering applications.
• Model non-Newtonian and complex fluid rheology modelling, including high-viscosity, shear-thinning, or particle-laden flow regimes
• 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)
• Own meshing generation and strategy for complex industrial geometries, including rotating machinery, internal flow passages, spargers, and free-surface interfaces
• Build robust parametric CAD models (NX, CATIA, or equivalent) tightly coupled with simulation pipelines, enabling automated design optimisation and DoE studies
• Multi-physics model development end-to-end: geometry clean-up, meshing, solver setup, post-processing, and experimental data integration for model validation
• Scale-up/scale-down methodology — translate small-scale experimental data to full-scale CFD models and iterating model fidelity against physical measurements
• 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
• 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
• 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
• Experimental validation workflows — compare simulation predictions against physical test data, interpreting discrepancies, and systematically improving model fidelity
• 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

What we offer

Build what actually matters

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.

Learn alongside exceptional people

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.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.

Sustainable pace, long-term ambition

Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

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 employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.