Research Assistant, AI Driven Photonics Design Engineer …, Melbourne

Position: AI Driven Photonics Design Engineer About the Role We are seeking an engineer to develop next‑generation photonic devices using artificial intelligence, inverse design methods, and physics‑based modelling. The role is embedded within the ARC E2Crop Hub and the Centre for Atomaterials and Nanomanufacturing at RMIT University. The successful candidate will develop machine learning approaches to design and optimise integrated photonic devices and metasurfaces, building automated workflows that link electromagnetic simulation tools with AI models to accelerate device discovery and performance optimisation. The role involves applying computational methods such as topology optimisation, adjoint techniques, and data‑driven modelling to identify high‑performance, manufacturable photonic structures while accounting for fabrication limits and multi‑physics effects. You will also contribute to internal software tools that support rapid prototyping and enable broader research teams to use AI‑driven design methods. The position offers access to advanced computational infrastructure and fabrication facilities. Key Responsibilities
- Develop machine learning approaches, including generative models and physics‑informed neural networks, to design and optimise photonic devices and metasurfaces.
- Build automated workflows that integrate electromagnetic simulation tools with AI models.
- Apply computational methods (topology optimisation, adjoint techniques, data‑driven modelling) to identify manufacturable photonic structures.
- Contribute to internal software tools for rapid prototyping and AI‑driven design method deployment. Qualifications
- Ph.D. in Optical Engineering, Applied Physics, Computer Science, or related field.
- Publication record in top‑tier journals demonstrating machine learning applied to physical problems.
- Experience with surrogate modelling, lithography limitations, and design‑for‑manufacturability (DFM).
- Deep understanding of waveguide optics, light‑matter interaction, and plasmonics.
- Proficiency with electromagnetic simulation tools (e.g., Lumerical FDTD/MODE, COMSOL Multiphysics, Ansys HFSS, Meep).
- Experience with photonic integrated device design flow and foundry PDKs (Silicon Photonics, SiN or III‑V).
- Strong proficiency in Python and machine learning frameworks (PyTorch, TensorFlow, JAX).
- Experience with photonic design techniques and global optimisation algorithms (Genetic Algorithms, Particle Swarm, Gradient Descent).
- Experience implementing deep learning architectures relevant to physics (CNNs, Graph Neural Networks, Physics‑Informed Neural Networks).
- Solid grasp of numerical methods, PDEs, linear algebra, HPC or cloud‑based simulation acceleration.
- Evidence of research output, including publications, conference contributions and technical reports.
- Ability to generate alternative funding projects and collaborate across teams.
- Strong communication skills and ability to meet deadlines and manage varying workloads. Additional Requirements
- Appointment is subject to passing a Working with Children and National Police Check. Equal Opportunity Statement We are an equal chance employer committed to being a child‑safe organisation and welcoming applicants from all sectors of the community. #J-18808-Ljbffr Apply on Kit Job: kitjobau.com/job/3rtt1r