{"id":1253072,"url":"https://alion.io/job/astar-research-engineer-semiconductor-iaic","title":"Research Engineer, Semiconductor, IAIC","company":{"id":50235,"name":"Agency for Science, Technology and Research","domain":"a-star.edu.sg","url":"https://alion.io/company/a-star-edu","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"B","score":75,"open_postings":36,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"JAX","optional":true},{"name":"Linux","optional":true},{"name":"PyTorch","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-09-25T18:38:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-17T21:45:37Z","board_verified":true,"closed_at":null,"days_open":22,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":22},"description":"Job Summary\nWe 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.\nThe 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.\nKey Responsibilities\nDevelop physics-guided AI and machine-learning models for photonic-device and semiconductor-process modelling. \nPerform electromagnetic, optical, lithography, etch, and semiconductor-device simulations using tools such as FDTD, FEM, RCWA, TCAD, and lithography simulators. \nDevelop AI-assisted inverse-design and optimization workflows for photonic structures under optical-performance and fabrication constraints. \nSupport lithography-etch OPC development, including layout processing, lithography prediction, etch-bias modelling, SEM-data analysis, pattern correction, and post-fabrication validation. \nCollaborate with researchers, process engineers, fabrication teams, and external partners to translate research outcomes into practical demonstrators and manufacturing solutions. \nPrepare technical reports, presentations, software documentation, and research publications. \nQualifications\nBachelor’s or Master’s degree in Electrical or Electronic Engineering, Physics, Photonics, Materials Science, Mechanical Engineering, Computer Engineering, or a related discipline. \nCandidates with a relevant PhD may also be considered, subject to experience and position requirements. \nInterested applicants with no experience but with strong project experience are welcome to apply. \nTechnical Requirements\nGood understanding of electromagnetics, optics, photonics, semiconductor physics, or semiconductor manufacturing processes. \nExperience with one or more numerical simulation methods, such as FDTD, FEM, RCWA, BPM, Fourier optics, TCAD, or lithography simulation. \nProficiency in Python for scientific computing, data analysis, and simulation automation. \nBasic knowledge of machine learning and optimization methods. \nPreferred Skills\nExperience with simulation platforms such as Ansys Lumerical, COMSOL, MEEP, Zemax OpticStudio, Sentaurus TCAD, Prolith, or equivalent tools. \nFamiliarity with AI frameworks such as PyTorch or JAX. \nKnowledge of physics-informed neural networks, surrogate modelling, Bayesian optimization, reinforcement learning, topology optimization, adjoint optimization, or generative design. \nUnderstanding of design-for-manufacturing principles for wafer-scale nanophotonics and semiconductor fabrication. \nExperience with Linux, version control, high-performance computing, or parallel simulation workflows would be 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