Applied Scientist - All Levels

Company Overview

PhysicsX is a pioneering physics AI company aiming to revolutionize industrial engineering and manufacturing by accelerating hardware innovation. The company is developing advanced simulation software to enable deep physics AI integration throughout the engineering lifecycle. With a focus on aerospace & defence, automotive, semiconductors, materials, and energy & renewables industries, PhysicsX partners with top organizations to tackle critical engineering challenges globally. Headquartered in the UK, the company maintains offices in London, New York, Singapore, and the Bay Area.

Role Summary

PhysicsX is establishing a research team in Singapore dedicated to creating physical foundation models in collaboration with customers and partners, focused on transformative engineering domains.

Key Responsibilities

• Collaborate closely with machine learning engineers, simulation engineers, customers, and partners to convert complex physics and engineering problems into mathematical formulations.
• Develop predictive models for physical systems employing cutting-edge machine learning techniques capable of handling large datasets, supported by thorough experimentation.
• Analyze trade-offs under conditions of limited information to make strategic decisions, such as balancing model size against data generation.
• Lead research initiatives at various levels according to seniority.
• Engage with colleagues and customers to discuss findings and practical implications of research.
• Present work internally and externally, including academic publications, industry workshops, and client meetings.
• Encourage curiosity and proactive behaviors among team members and mentees.

Candidate Profile

• Passionate about applying machine learning, particularly deep learning and probabilistic techniques, to scientific and engineering challenges.
• Capable of scoping projects effectively and delivering results efficiently.
• Demonstrates strong analytical skills to diagnose problems swiftly and propose effective solutions.
• Exceptional communication and teamwork abilities for collaboration with diverse teams and stakeholders.
• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or related domains.
• Specialized expertise in one or more of the following areas:
  • Operator learning (neural operators) or probabilistic methods related to partial differential equations (PDEs).
  • Geometric deep learning or 3D computer vision approaches for point-cloud or mesh-based data.
  • Generative models for geometry and spatiotemporal datasets such as VAEs, diffusion models, Bayesian non-parametric models, scalable to extensive data.
• Preferably over two years of experience in data-centric roles with:
  • Developing machine learning models and workflows in Python using libraries like NumPy, SciPy, Pandas, PyTorch, and JAX, emphasizing deep learning.
  • Creating bespoke models tailored to high-dimensional data across spatiotemporal, geometric, or physical domains.
  • Iteratively refining neural network architectures for inductive bias, generalizability, and performance improvements.
  • Balancing theoretical insights and empirical experimentation to guide research.
  • Managing experimental pipelines to benchmark models and ensure reproducibility.
  • Skillful technical writing to convey complex ideas clearly to both technical and non-technical audiences.
• Proven publication history in respected conferences and journals such as NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR, TPAMI, JMLR, Nature, or Science.

What PhysicsX Offers

• Opportunity to make meaningful contributions by shaping AI-native engineering solutions with significant industrial and societal impact.
• Collaborative environment with expert scientists and engineers focused on excellence and innovation.
• Flat organizational structure encouraging idea meritocracy and open challenge of assumptions.
• Commitment to sustainable work-life balance supported by a hybrid work model blending office presence with remote flexibility.
• Strong dedication to diversity, equality, and inclusion, actively encouraging underrepresented groups to apply and sponsoring women from marginalised backgrounds in STEM education.
• Confidential collection of diversity data to support equal opportunity compliance and policy effectiveness.