Overview:

• 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

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 (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.

Qualifications

Mandatory:

• Ph.D. degree in Optical engineering, Applied Physics, or Computer Science

• Publication record in top-tier journals, demonstrating the application of machine learning to physical problems

• Experience with surrogate modelling to replace computationally expensive simulations Knowledge of lithography limitations and design-for-manufacturability (DFM).

• Problem-solving abilities for determining innovative solutions to the complex problems common to photonics design and fabrication.

• Attention to detail for ensuring precision in coding and model training – and enhancing the accuracy and reliability of AI modelling and data quality.

• Collaborative skills for working effectively with data scientists, software developers, and project managers.

• Adaptability, including a willingness to stay updated with the rapidly evolving field of frontier research and learn new techniques and technologies as they emerge.

• Communication skills for explaining technical project requirements, progress, and outcomes to non-technical stakeholders

Please Note: Appointment to this position is subject to passing a Working with Children and National Police Check.

To Apply

Please submit your CV and covering letter and address the Key Selection Criteria for this position by clicking on the ‘Apply’ link at the top of this page. For more information on this role, please connect with Dr. Mingdeng Luo, ARC E2Crop Hub Manager at mingdeng.luo2@rmit.edu.au

Position Description - Research Assistant, AI Driven Photonics Design Engineer

Please note, if you are viewing this advert from an external site, please click ‘apply’ and you will be redirected to RMIT's Jobs website to access the Position Description at the bottom of the page. Due to recent upgrades, position description links may not work on LinkedIn advertisements.

Applications Close:

RMIT is an equal opportunity employer committed to being a child safe organisation. We are dedicated to attracting, retaining and developing our people regardless of gender identity, ethnicity, sexual orientation, disability and age. Applications are encouraged from all sectors of the community, and we strongly encourage applications from the Aboriginal and/or Torres Strait Islander community.

In line with RMIT University’s commitment to a safe, respectful and inclusive environment, from 1 January 2026, the University will also consider gender-based violence (GBV) risk factors as part of our recruitment processes. All applicants will be required to complete a gender‑based violence declaration in accordance with the National Higher Education Code to Prevent and Respond to Gender-based Violence. Preferred candidates will also be required to undertake relevant pre‑employment background checks, including Working With Children Checks. Information provided will be treated confidentially and considered only for its relevance to the role and RMIT’s safety obligations.

At RMIT, we are committed to supporting adjustments throughout the recruitment and selection process, as well as during employment. We actively support and encourage people with disability to apply to RMIT (including alternate formats of application forms). To discuss adjustment requirements, please contact Dani, via talentsupport@rmit.edu.au or visit our Careers page for more contact information - https://www.rmit.edu.au/careers.

We are a Circle Back Initiative Employer – we commit to respond to every applicant.