Applied Scientist - All Levels

Company Overview

PhysicsX is a deep-technology company rooted in numerical physics and Formula One, committed to accelerating innovation in hardware development at software speed. The company develops AI-powered simulation software aimed at engineering and manufacturing within advanced industries.

PhysicsX’s technology enables high-accuracy multi-physics simulation via AI across the engineering lifecycle, allowing greater design, manufacturing, and operational optimization, with customers in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive sectors. The Singapore-based research team focuses on creating foundational physical models that will transform engineering domains.

Roles & Responsibilities

• Collaborate with machine learning engineers, simulation experts, clients, and partners to translate physical and engineering problems into mathematical frameworks.
• Create predictive models of physical system behaviors using cutting-edge machine learning methods capable of scaling across large datasets while iterating through rigorous experiments.
• Navigate complex decisions involving trade-offs such as balancing model size versus data volume.
• Lead research workstreams aligned with the candidate’s experience level.
• Analyze and discuss research outcomes with colleagues and clients to relate findings to practical issues.
• Present work internally and externally through publications, industry events, and client discussions.
• Promote curiosity and initiative within the team and mentees.

Qualifications & Experience

• Passionate about applying machine learning (especially deep learning and probabilistic techniques) in science and engineering contexts.
• Demonstrated ability to scope projects effectively and deliver outcomes successfully.
• Strong analytical and problem-solving competencies with quick identification of issues and proposing solutions.
• Excellent interpersonal and communication skills for effective collaboration with teams and clients.
• PhD in fields such as computer science, machine learning, applied statistics, mathematics, physics, engineering, or related disciplines with expertise in areas like:
  • Neural operators or probabilistic approaches for partial differential equations (PDEs).
  • Geometric deep learning or 3D computer vision techniques for point cloud or mesh data.
  • Generative modeling techniques for geometry and spatiotemporal data including VAEs, diffusion models, and Bayesian non-parametrics at scale.
• Preferably over 2 years in data-centric roles involving:
  • Development of machine learning models and pipelines using Python and frameworks such as NumPy, SciPy, Pandas, PyTorch, or JAX.
  • Modeling bespoke problems with high-dimensional, spatiotemporal, geometric, or physical data.
  • Iterative design and tuning of network architectures to enhance inductive biases, generalizability, and performance.
  • Combining theory and empirical insight to guide experimentation.
  • Planning and conducting experiments to benchmark models and ensure comparability.
  • Strong writing skills for translating complex technical content to diverse audiences.
• A solid publication record in top-tier conferences and journals like NeurIPS, ICML, ICLR, CVPR, Nature, or Science is a plus.

Benefits & Culture

• Opportunity to work on technology with significant industry and societal impact, shaping an AI-native engineering company at an early stage.
• Collaborate alongside a high-performing, diverse team committed to excellence and mutual growth.
• Flat organizational structure promoting open idea exchange and challenging assumptions.
• Balanced work pace supporting long-term sustainability with a hybrid work model combining office and remote days.
• Commitment to diversity and equal opportunity employment, encouraging applicants from underrepresented groups and supporting women from disadvantaged backgrounds through education sponsorship.

Diversity & Inclusion Statement

PhysicsX values diversity and monitors the effectiveness of its equal opportunity policies confidentially and in aggregate, ensuring a fair recruitment process compliant with employment legislation.