Research Fellow (Laboratory for Advanced Materials Synthesis)

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal. NUS Career Portal link - https://careers.nus.edu.sg/job/Research-Fellow-%28Laboratory-for-Advanced-Materials-Synthesis%29/34552-en_GB/ We regret that only shortlisted candidates will be notified. Job Description The Institute for Functional Intelligent Materials (I-FIM) at the National University of Singapore is establishing a new state-of-the-art material robotics laboratory dedicated to the advanced synthesis and testing of functional materials, AI-driven autonomous experimental design, and robotic automation. We are seeking a highly motivated materials expert to (1) lead the strategic development and day-to-day operations of the material robotics laboratory, and (2) oversee the operation and management of an established furnace cluster. Experience in the synthesis of low-dimensional materials and thin films using high-temperature growth techniques is highly preferred. This role offers a unique opportunity to shape a cutting-edge facility at the forefront of materials discovery and to work closely with interdisciplinary teams spanning computation, automation, and synthesis. Key Responsibilities • Key equipment to manage: A fully automated metal-organic chemical vapor deposition (MOCVD) platform, integrated with in-situ testing equipment and supported by gas-handling systems, scrubbers, and a glovebox for safe handling of air-sensitive chemicals and precursors in the material robotics laboratory. Multiple furnaces (tube, muffle), ovens and reactors in the furnace cluster. • Strategic Leadership & Vision - Support the development and execution of a research roadmap for AI-guided autonomous synthesis of novel functional materials using CVD and high temperature material synthesis platforms • Laboratory Setup & Management - Oversee the commissioning of furnaces, robotic platforms, and digital infrastructure for synthesis automation. Develop standard operating procedures (SOPs), safety protocols, and maintenance schedules. • Safety & Compliance - Develop RAs, SOPs, and safety procedures; manage chemical, gas, high-temperature, vacuum, and equipment hazards; ensure EHS compliance; conduct inspections, training, and corrective actions. • Equipment Management & Reliability - Manage the equipment lifecycle from procurement and commissioning through maintenance and decommissioning; coordinate FAT/SAT, qualification, calibration, servicing, and troubleshooting • Technical & Engineering Support - Troubleshoot equipment and process issues; work with researchers, engineers, and OEMs; develop custom solutions and drive equipment/process improvements • Procurement & Vendor Management - Develop technical specifications, evaluate proposals, manage procurement and acceptance, coordinate vendors, and support equipment and maintenance budgets • Collaboration & Supervision - Collaborate with researchers across synthesis, computation, automation, and characterization to implement closed-loop materials discovery workflows • AI Integration - After validating manual experiments, work closely with the institute’s AI team to integrate machine learning and autonomous decision-making into the synthesis workflow. Assist in developing or utilizing active learning strategies to optimize synthesis conditions Job Requirements • PhD in Materials Science, Chemistry, Physics, or related field • Requirement of a PhD might be waived off if the candidate comes with substantial relevant industry experience • Strong track record in solid-state or thermal synthesis techniques (e.g., CVD, tube furnace synthesis, rapid thermal annealing, ampule synthesis, self-flux, CVT, etc) • Experience in establishing new lab infrastructure or managing complex research projects • Proven ability to manage a research group or laboratory setting • Excellent communication and collaborative skills • Bonus to have familiarity with lab automation, robotic platforms, or in-situ diagnostics is highly desirable • Bonus to have demonstrated experience with AI/ML integration in materials science or a strong interest in learning and applying these tools