Job Summary
We are seeking a highly motivated Research Engineer to support the development of AI-driven modelling and simulation technologies for advanced photonics and semiconductor applications. The successful candidate will work at the intersection of physics-based simulation, artificial intelligence, inverse design, and design-for-manufacturing.
The research scope will include advanced photonics, power electronics, and lithography–etch optical proximity correction. The candidate will contribute to translating emerging research concepts into practical device designs and manufacturing-ready solutions under Singapore’s semiconductor and photonics research initiatives, including NSTIC and IAIC programmes within A*STAR.
Key Responsibilities
- Develop physics-guided AI and machine-learning models for photonic-device and semiconductor-process modelling.
- Perform electromagnetic, optical, lithography, etch, and semiconductor-device simulations using tools such as FDTD, FEM, RCWA, TCAD, and lithography simulators.
- Develop AI-assisted inverse-design and optimization workflows for photonic structures under optical-performance and fabrication constraints.
- Support lithography–etch OPC development, including layout processing, lithography prediction, etch-bias modelling, SEM-data analysis, pattern correction, and post-fabrication validation.
- Collaborate with researchers, process engineers, fabrication teams, and external partners to translate research outcomes into practical demonstrators and manufacturing solutions.
- Prepare technical reports, presentations, software documentation, and research publications.
Qualifications
- Bachelor’s or Master’s degree in Electrical or Electronic Engineering, Physics, Photonics, Materials Science, Mechanical Engineering, Computer Engineering, or a related discipline.
- Candidates with a relevant PhD may also be considered, subject to experience and position requirements.
- Fresh graduates with strong project experience are welcome to apply.
Technical Requirements
- Good understanding of electromagnetics, optics, photonics, semiconductor physics, or semiconductor manufacturing processes.
- Experience with one or more numerical simulation methods, such as FDTD, FEM, RCWA, BPM, Fourier optics, TCAD, or lithography simulation.
- Proficiency in Python for scientific computing, data analysis, and simulation automation.
- Basic knowledge of machine learning and optimization methods.
Preferred Skills
- Experience with simulation platforms such as Ansys Lumerical, COMSOL, MEEP, Zemax OpticStudio, Sentaurus TCAD, Prolith, or equivalent tools.
- Familiarity with AI frameworks such as PyTorch or JAX.
- Knowledge of physics-informed neural networks, surrogate modelling, Bayesian optimization, reinforcement learning, topology optimization, adjoint optimization, or generative design.
- Understanding of design-for-manufacturing principles for wafer-scale nanophotonics and semiconductor fabrication.
- Experience with Linux, version control, high-performance computing, or parallel simulation workflows would be advantageous.