Materials Science Jobs

PhysicsX · London

Builds and validates multiphase CFD and coupled CFD-DEM models, from geometry clean-up and meshing through simulation, post-processing and experimental validation, covering fluidised beds, pneumatic conveying, mills and coating processes. Automates parametric CAD and simulation pipelines for DoE and optimisation, generating datasets for ML surrogates, and works on-site with customers using cloud and HPC resources.

PhysicsX · Singapore

Builds and applies advanced thermal, chemical and multiphase CAE models in Abaqus, ANSYS or COMSOL, coupling heat transfer with fluid, diffusion and electrostatic phenomena. Works directly with customers, often on-site, instrumenting physical processes to generate validation data and integrating models into AI tools. Combines shop-floor manufacturing experience with Python/Java automation and parametric CAD.

PhysicsX · Singapore

Customer-facing role owning the full customer journey, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists to translate industry challenges into product opportunities, tracking engagement and measuring value. Suits a technical background in data science/ML or engineering.

PhysicsX · New York, United States

Builds and runs high-fidelity CFD models from geometry clean-up and meshing through simulation and post-processing, using Star-CCM+, OpenFOAM or Fluent plus parametric CAD and Python/Java automation. Works with data scientists and ML engineers to generate simulation datasets for deep learning surrogates, running jobs on cloud and HPC, and supports customer engagements with occasional travel.

PhysicsX · Singapore

Join a new Singapore research team building physical foundation models for industrial engineering, translating physics and simulation challenges into mathematical formulations. You will train deep learning and probabilistic models on large spatiotemporal, geometric and point-cloud datasets, run experiment pipelines, and publish and present results with customers and partners.

PhysicsX · Singapore

Build physical foundation models for industrial engineering, scaling training to large datasets on multi-GPU cloud compute. Turn research prototypes into robust, optimised implementations, own research workstreams, and translate results into reusable libraries and tooling alongside scientists and customers.

PhysicsX · London

Owns the editorial calendar and writes blog posts, web pages, thought leadership, scripts and event materials, translating deep technical work into clear narratives. Leads LinkedIn and Instagram, supports executive and technical speaking engagements, and partners with engineers, designers and external agencies on video and joint content.

PhysicsX · London

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.

PhysicsX · Singapore

Deliver high-fidelity multi-physics CFD simulations for industrial customers, from geometry clean-up and meshing to post-processing and validation against experimental data. Build parametric CAD models and automate simulation pipelines for design optimisation and DoE studies, generating datasets for machine learning surrogates. Work alongside data scientists and ML engineers, mentor junior colleagues, and travel globally up to 2-3 weeks per quarter to work on-site with customers.

PhysicsX · Singapore

Lead technical delivery of physics AI solutions, architecting deep learning models and optimisation methods to predict and control physical systems. Work closely with simulation engineers and customers, building scalable production data pipelines and mentoring junior team members. Requires frequent travel to customer sites across multiple regions.

PhysicsX · Singapore

Lead the deployment of ML models and engineering surrogates—deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, and optimization—into customer production environments. Own scoping, architecture, and success metrics for data/ML systems built with Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow across cloud and on-prem. Mentor engineers and data scientists while travelling 3-4 weeks per quarter to customer sites worldwide.

PhysicsX · Singapore

Builds and deploys customer-facing applications that put physics AI models into production, working directly with customers, data scientists and ML engineers. Involves rapid prototyping, RESTful APIs in Python (FastAPI), microservices and cloud deployment with Kubernetes, Helm, Terraform and AWS/Azure. Spans the full stack across complex engineering domains.