PhysicsX
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About
PhysicsX provides an AI platform designed to transform the engineering of physical systems by embedding intelligence throughout the entire product lifecycle. The system is intended to assist in stages ranging from initial concepting and design to manufacturing and service operations. By utilizing AI-driven multiphysics inference alongside traditional numerical simulation, the platform aims to accelerate development timelines and reduce the risks associated with high-stakes engineering projects. This approach targets the limitations of conventional simulation methods, which are often computationally expensive and siloed. The platform functions as a centralized environment where enterprises can develop, deploy, and scale specialized AI tools. It handles the complete AI lifecycle, including simulation data management, model training, and fine-tuning. One of the primary features is the ability to create agentic applications that can autonomously navigate large design spaces to identify optimal configurations. These tools allow engineers to evaluate performance in real-time, enabling faster iteration cycles than were previously possible with legacy hardware and software setups. This technology is primarily built for professionals within the aerospace, defense, semiconductor, and renewable energy industries. It serves roles such as senior data scientists and research engineers who are tasked with solving complex problems related to industrial resilience and the climate transition. The platform's ability to integrate real-world experimental data with generative design methods makes it suitable for optimizing components in semiconductors or improving the structural integrity of aerospace hardware. A key differentiator for PhysicsX is its focus on sovereign AI infrastructure through partnerships with major technology providers. The platform is integrated with the Microsoft Agentic Launchpad and the European Industrial AI Cloud, utilizing NVIDIA's computational architecture. These collaborations provide users with the high-performance computing power necessary for advanced multiphysics modeling while maintaining data sovereignty. This infrastructure supports a transition toward AI-native engineering workflows that continuously improve through data feedback loops.
Pros & Cons
Accelerates development by replacing slow traditional simulations with real-time AI inference.
Supports the entire product lifecycle from initial concept to in-service operations.
Integrates with major ecosystems like NVIDIA and Microsoft for advanced AI infrastructure.
Capable of exploring large design spaces that are beyond manual human optimization.
High industry specificity for semiconductors, aerospace, and energy sectors.
Primarily targeted at large-scale industrial enterprises, which may be inaccessible for smaller firms.
Requires significant proprietary engineering datasets for effective model training and fine-tuning.
Focuses on complex physical systems which may involve a steep learning curve for non-specialists.
Use Cases
Aerospace engineers can use the platform to optimize aerodynamic components and reduce weight through generative design and real-time simulation.
Automotive R&D teams can accelerate vehicle performance testing by integrating AI-driven simulations into their existing manufacturing workflows.
Renewable energy developers can improve the efficiency of power generation systems by modeling complex physical interactions in real-time.
Platform
Task
Features
• generative design exploration
• model fine-tuning
• sovereign ai infrastructure
• lifecycle data management
• agentic application deployment
• real-time automated optimization
• numerical simulation integration
• ai-driven multiphysics inference
FAQs
How does PhysicsX differ from traditional simulation software?
Traditional methods are often slow and resource-intensive, whereas PhysicsX uses AI-driven multiphysics inference to provide real-time results. This allows for much faster iterations and the ability to automate optimization across massive design spaces.
What industries can benefit from the PhysicsX platform?
The platform is specifically tailored for high-impact sectors including semiconductors, aerospace, defense, materials science, automotive, and renewable energy. It addresses complex engineering challenges unique to these fields.
Can I integrate PhysicsX with my existing engineering data?
Yes, the platform is designed to manage simulation data and continuously learn from real-world results to enhance model accuracy. It supports the full lifecycle from data management to model deployment.
Does PhysicsX offer any integration with cloud providers?
PhysicsX has significant partnerships with Microsoft and Deutsche Telekom to deliver AI infrastructure. It is also available on the European Industrial AI Cloud, leveraging NVIDIA technology.
What is an agentic application in the context of PhysicsX?
Agentic applications are customizable AI tools deployed via the platform that can autonomously handle complex engineering tasks. These tools help scale AI-native engineering by automating aspects of design and simulation.
Job Opportunities
CFD Simulation Intern
Optimize complex physical systems and accelerate industrial innovation with AI-driven multiphysics simulations for aerospace, automotive, and energy engineers.
Benefits:
Paid opportunity
Work with a fun group of colleagues
Technical growth
Challenging problems
Education Requirements:
Currently pursuing a PhD (or final year Masters) degree in Mechanical Engineering, Civil Engineering, Aerospace Engineering, Chemical Engineering or Physics
Experience Requirements:
Coursework or demonstrated knowledge in fluid mechanics, heat transfer and chemistry
Familiarity with CFD software Star-CCM+, ANSYS Fluent, COMSOL or OpenFOAM
Familiarity with parametric CAD design on NX, CATIA or FreeCAD
Familiarity with HPC environments and Linux
Familiarity with AI-driven modelling techniques
Other Requirements:
Must have existing right to work in the UK (no visa sponsorship)
Strong problem-solving skills
Excellent communication skills
Responsibilities:
Develop parametric CAD models and geometry generation workflows
Build automated CFD pipelines for high-fidelity datasets
Manage large-scale simulation datasets on cloud HPC
Implement automated post-processing routines
Collaborate with Data Scientists and ML Engineers
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Data Scientist
Optimize complex physical systems and accelerate industrial innovation with AI-driven multiphysics simulations for aerospace, automotive, and energy engineers.
Benefits:
Equity options
5% 401(k) match
Flexible working
Enhanced parental leave
Private healthcare
Education Requirements:
Masters or PhD
Experience Requirements:
At least 1 year industry experience in a commercial, non-research environment
Foundations in data driven modelling and deep learning
Hands-on experience in probabilistic methods and predictive modelling
Expertise in Python (NumPy, SciPy, Pandas, TensorFlow, PyTorch)
Deploying scalable, production-ready models
Other Requirements:
Ability to travel to customer sites (2-3 weeks per quarter)
Right to work status required
Responsibilities:
Pre-processing and analyzing data for predictive modelling
Developing innovative deep learning models
Designing, building and testing data pipelines
Integrating data science models with simulations
Engaging in communication and presentation with technical teams and customers
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Delivery Engineer
Optimize complex physical systems and accelerate industrial innovation with AI-driven multiphysics simulations for aerospace, automotive, and energy engineers.
Benefits:
Diversity and inclusion commitment
University sponsorship for bright women from disadvantaged backgrounds
Education Requirements:
Technical background (Data Science, Machine Learning, or Engineering)
Experience Requirements:
At least 3-5 years industry experience in a commercial, customer-facing environment
End-to-end AI/ML product lifecycle understanding
Strategic planning, time management, and problem-solving
Other Requirements:
Right to Work status required
Keen learner
Responsibilities:
Build and maintain relationships with users and decision-makers
Translate customer industry challenges into product opportunities
Own the customer onboarding journey
Support the running of customer delivery teams
Find the next useful thing to build for a customer
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