Senior Scientific Software Engineer - Simulation and Machine Learning

Pillar 1 Simulation Engine

• Build and maintain high-performance simulation code for open and closed quantum systems.
• Own the differentiable simulation and automatic differentiation layer.
• Drive performance. This code runs inside optimisation loops and speed decides what is possible.
• Maintain and extend the Julia core and its Python interface.

Success Metrics and KPIs: simulation accuracy against measurement, runtime performance, API stability.

Pillar 2 Model Learning and Optimisation

• Implement parameter inference and model learning from experimental data.
• Work on neural-network approaches to PDE solving and equation discovery.
• Benchmark against established academic tools and publish the comparison.

Success Metrics and KPIs: benchmark results, methods adopted by the research group, publications.

Pillar 3 Research Collaboration

• Work with Constructor University faculty on joint research and grant applications.
• Supervise graduate students and interns working on the engine.
• Contribute to course material on numerical simulation and machine learning for physics.

• PhD or equivalent in physics, applied mathematics or computational science.
• Strong scientific computing in Julia, Python, JAX or comparable.
• Real numerical depth: ODE solvers, optimisation, stochastic processes, automatic differentiation.
• Published work, or code that others rely on.

Skills & Competencies

• Able to take a method from a paper and make it fast, tested and usable.
• Comfortable owning code quality, not only correctness.
• Interest in supervising students alongside the engineering.
• Clear technical writing.

Education & Languages

• PhD in physics, applied mathematics or computational science.
• Fluent English.