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Applied Scientist - All Levels

PhysicsX· Research
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Hybrid (Singapore)full-timePosted 3mo ago

About the role

PhysicsX is starting a research team in Singapore to build physical foundation models for engineering domains, partnering with customers and industry to translate physics and engineering challenges into ML solutions and scalable physics AI capabilities.

Responsibilities

  • Work closely with 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., bigger model vs 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.

Qualifications

  • PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with expertise in: 1) operator learning (neural operators) or probabilistic methods for PDEs; 2) geometric deep learning or 3D computer vision for point-cloud or mesh data; 3) generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets).
  • Ideally, >2 years of experience in a data-driven role with exposure to building ML models and pipelines in Python (NumPy, SciPy, Pandas, PyTorch, JAX), including deep learning; developing models for high-dimensional data (spatiotemporal, geometric, physical); iterating on architectures and inductive biases; combining theory with empirical intuition; running experiment pipelines to benchmark models; writing skills for communicating complex concepts.
  • Publication record in reputable venues (NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR, TPAMI/JMLR, Nature/Science) demonstrating mastery in relevant domains.

Skills

Required:Machine learning and deep learningProbabilistic methods for PDEsPython ML stackExperiment design and benchmarkingCommunication and collaboration
Nice to have:Geometric deep learning / 3D computer visionGenerative models for geometry and spatiotemporal data

Benefits

  • Hybrid work model blending Shoreditch office days with work-from-home days.
  • Diversity and equal opportunity commitment, sponsorship programs for underrepresented groups and disadvantaged backgrounds in science and mathematics.
  • Opportunity to work on high-impact problems at the intersection of physics and ML.
Last updated Sep 23, 2026. Interested? Apply on the company site.

About PhysicsX

Accelerating Industrial Innovation with AI

PhysicsX is deploying AI to transform how physical systems are engineered, embedding intelligence across the entire product lifecycle, from concepting and design to manufacturing and operations.

AIIndustrial InnovationDesignManufacturingOperationsAI ToolsProduct Lifecycle
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