Research Scientist

PhysicsX

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. PHYSICSX IS STARTING A RESEARCH TEAM IN SINGAPORE TO BUILD PHYSICAL FOUNDATION MODELS ALONGSIDE OUR CUSTOMERS AND PARTNERS, TARGETING ENGINEERING DOMAINS WHERE THIS CAPABILITY WILL BE MOST TRANSFORMATIVE. WHAT YOU WILL DO  * Work closely with our research scientists, simulation engineers, customers and partners to deliver AI models that address real-world physics and engineering problems. * Design and build physical foundation models with a focus on efficiently scaling model training to large data on multi-GPU cloud compute. * Transform prototypes from your research scientist colleagues into robust and optimised implementations, challenging architecture decisions that hurt scalability. * Identify and argue for the best libraries, frameworks and tools to set us up for success. * Own Research work-streams at different levels, depending on seniority. * Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems. * Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products. * Foster curiosity and initiative among your colleagues and mentees.   WHAT YOU BRING TO THE TABLE * Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering. * Ability to work autonomously and scope and effectively deliver projects across a variety of domains. * Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. * Excellent collaboration and communication skills — with teams and customers alike. * MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following: * Scientific computing; * High-performance computing (CPU / GPU clusters); * Parallelised / distributed training for large / foundation models. * Ideally, >2 years of experience in a data-driven, professional setting, with exposure to: * scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus); * distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton); * cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP); * building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; * C/C++ for computer vision, geometry processing, or scientific computing; * software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps); * container-ization and orchestration (Docker, Kubernetes, Slurm); * writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.   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 UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

PhysicsX

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. PHYSICSX IS STARTING A RESEARCH TEAM IN SINGAPORE TO BUILD PHYSICAL FOUNDATION MODELS ALONGSIDE OUR CUSTOMERS AND PARTNERS, TARGETING ENGINEERING DOMAINS WHERE THIS CAPABILITY WILL BE MOST TRANSFORMATIVE. WHAT YOU WILL DO  * Work closely with our machine learning engineers, simulation engineers, customers and partners to translate physics and engineering challenges into mathematical problem formulations. * Build models to predict the behaviour of physical systems using state-of-the-art machine learning techniques that scale to large datasets, iterating through robust experimentation. * Chart a path through competing trade-offs with insufficient information, e.g. is it better to train a bigger model or to generate more data? * Own Research work-streams at different levels, depending on seniority. * Discuss the results and implications of your work with colleagues and customers, connecting with real-world problems. * Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. * Foster curiosity and initiative among your colleagues and mentees.   WHAT YOU BRING TO THE TABLE * Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering. * Ability to scope and effectively deliver projects. * Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. * Excellent collaboration and communication skills — with teams and customers alike. * PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following: 1. operator learning (neural operators), or other probabilistic methods for PDEs; 2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data; 3. generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.). * Ideally, >2 years of experience in a data-driven role, with exposure to: * building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; * developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical); * iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance; * combining theoretical reasoning with empirical intuition to guide investigation; * formulating and running experiment pipelines to benchmark models and produce comparable results; * writing skills for communicating complex technical concepts to peers and non-peers, tailoring the message for the required audience. * Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to): NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR, TPAMI/JMLR, Nature and Science.   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 UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

Nomagic

Do you believe the path to general-purpose physical AI runs through noisy, real-world factory deployments? Are you excited by the challenge of turning the classical robotic stacks into the foundational training data for physical AI? Do you want to bridge the gap between world-class ML research and industrial-scale robotic execution? If your answers are yes, we should talk. At Nomagic, we are executing a humble pivot for general-purpose physical AI. We believe that physical AI is fundamentally a knowledge transfer problem - we are leveraging the "internet data" of robotics - massive deployment logs from real systems operating in production environments -  to bootstrap our efforts. We are looking for Research Scientists who will help us to build, train, and deploy foundational models that bring our fleet from a classical control stack to generalized AI mastery.