Research Assistant, AI Driven Photonics Design Engineer

About the role

1 x full time, fixed term (2 years) position available in the School of Science within STEM College Salary Academic Level A ($80,755 - $109,536) + 17% Superannuation Based at the City, but may be required to work and/or be based at other campuses of the University 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 (RMIT University), which focus on renewable energy systems, intelligent sensing, advanced materials, and nanoscale device technologies. The successful candidate will develop machine learning approaches (including generative models and physics-informed neural networks) to design and optimise integrated photonic devices and metasurfaces. This includes 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, supporting research at the intersection of photonics, AI, and nanotechnology. About You Deep understanding of waveguide optics, light-matter interaction, and plasmonics Proficiency with electromagnetic simulation tools (e.g., Lumerical FDTD/MODE, COMSOL Multiphysics, Ansys HFSS, or Meep) Familiarity 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, or JAX) Experience with photonic design techniques and global optimisation algorithms (Genetic Algorithms, Particle Swarm, Gradient Descent) Experience implementing deep learning architectures relevant to physics, such as CNNs for image-based field prediction, Graph Neural Networks (GNNs), or Physics-Informed Neural Networks (PINNs) Solid grasp of numerical methods, partial differential equations (PDEs), and linear algebra Experience with high-performance computing (HPC) or cloud-based simulation acceleration Experience with integrated photonic device fabrication and light-matter interactions Evidence of research output including high quality publications, conference contributions and/or technical reports in the field. Ability to generate alternative funding projects through effective liaison with industry and government. Ability to work autonomously whilst displaying a strong commitment to work in a team environment, including the demonstrated ability to confidently and effectively work with colleagues, project team leaders, and industry partners. Demonstrated ability to meet deadlines and effectively manage varying workloads and respond to changing priorities as required. Demonstrated high level of communication skills.