Universitas Terbuka - Indonesia Open University

At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships forged by our faculty with industry, learners bridge theoretical knowledge with practical application in and out of the classroom. Such active & authentic learning opportunities ensures graduates step into their careers with applicable skills and competence.  In research, SIT faculty aims to bridge knowledge creation and utilisation. Our faculty collaborates closely with industry on translational research and innovation with focus on addressing pertinent industry challenges to impact businesses.  The Singapore Teaching and Academic Research Talent (START) Scheme is co-funded by the Ministry of Education (MOE) and SIT to encourage and support outstanding young Singaporeans interested in pursuing an academic career at SIT. The Early Career Awards (ECA) are designed to support Singaporeans who have recently completed their postdoctoral training and prepare them for an academic career at SIT. This award aims to enhance the teaching capabilities of the recipient and help build a strong research portfolio. Eligibility and Selection Criteria Singapore Citizen   Recently completed postdoctoral training   Excellent academic record plus a high degree of proficiency and motivation for a research area that is strongly aligned with SIT’s supported areas  Strong passion for teaching, research and an academic career Benefits Awardees may receive research grants to help build a competitive research portfolio. Upon completion of the award, awardees will be considered for Assistant Professorship. Awardees will also be assigned a faculty mentor for the duration of the scheme. Application Process Applications are open throughout the year. Applications received before 15 January 2026 will be considered for intake 2026.  Interview by the respective clusters for shortlisted applicants will be conducted throughout the year.  Final Interview by Panel for shortlisted applicants will take place from March to April 2026. Awards for intake 2026 will be finalised by July 2026. Supported Areas for START Food, Chemical and Biotechnology Sustainable and Functional food: Sustainable food systems & circular food economy - alternative proteins, upcycled ingredients); AI & automation in food manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for microbial bioprocess development; microbial physiology and adaptation for industrial applications; high-throughput screening and automation for strain optimization; bioprospecting for novel microbial hosts, enzymes, and metabolites to support sustainable biomanufacturing. (Bio)pharmaceuticals: Processing science and technology for bio- and chemical transformations, separations, purifications and formulation applied to pharmaceuticals manufacture; modelling of processing steps and whole processes to support innovation, design, optimisation and control for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process analytical technology (PAT); process intensification and sustainability.

Job Purpose As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.  The primary responsibility of this role is to deliver on industry innovation research where you will support all aspects of the project to investigate neuroplasticity and recovery of movement quality after stroke using multimodal functional brain imaging and 3D human kinematics. This funded research brings together multidisciplinary expertise from Singapore Institute of Technology (SIT), National University of Singapore (NUS), National University Hospital (NUH), University of Glasgow (UOG), Queen Elizabeth University Hospital (QEUH), Glasgow Royal Infirmary (GRI) and Nanyang Technological University (NTU) in the areas of Neuroscience, Psychology, Movement Science and Rehabilitation. Key Responsibilities Participate in and manage the research project with Principal Investigator (PI) and the research team members to ensure all project deliverables are met. Prepare data collection forms/interview questions/questionnaire and recruit and/or interview subjects. Maintain accurate records of interviews, safeguarding the confidentiality of subjects, as necessary. Perform computational modelling and analysis of the neuroimaging and behavioural data, with guidance from senior colleagues as required. Provide ready access to all experimental data for the faculty researcher and/or supervisor. Prepare materials for submission to granting agencies, foundations and Ethics Committee review. Prepare progress reports for the PI and funding agency. Prepare other articles, reports, and presentations. Coordinate and attend project meetings, monitor the project budget and work on other projects and initiatives, as required. Conduct literature reviews for publications and research briefings. Contribute to the dissemination of research findings (e.g., journal, conference, book). Support and collaborate with research assistants, graduate and undergraduate students, where applicable. Work in a team, contribute to the identification of possible funding sources and/or the planning and development of externally funded research proposals in an area relevant to the project. Perform other research-related tasks as required by the Principal Investigator. Job Requirements A good degree in relevant discipline (e.g. experimental psychology, biopsychology, cognitive psychology, neuropsychology, neurology, psychiatry, cognitive neuroscience, electrical engineering, biomedical engineering, computer science, physics or related fields). A PhD* (or equivalent research experience) in a relevant subject (only for Research Fellow position). Candidates with a non-healthcare degree are welcome to apply. Familiarity with image data analysis tools such as SPM, FSL, AFNI, and/or DSIStudio etc will be an advantage. Signal processing and programming skills (e.g. with Matlab, Python) will be an advantage. Ability to use statistical packages such as SPSS, R for statistical analyses and visualisation will be an advantage. Some research and/or clinical experience/interest in the area of stroke rehabilitation or care will be an advantage. Key Competencies Good scientific writing skills with relevant publications will be an advantage. Strong ability to work within a multidisciplinary team. Experience of supporting research projects and bringing them to successful completion. Possesses sound judgment and superior decision-making and problem-solving skills. Good interpersonal, written and verbal communication skills. Able to build and maintain strong working relationships with people within and external to the university. Proficient in technical writing and presentation. Possess strong analytical and critical thinking skills. Show strong initiative and take ownership of work. Organisation Chart The research staff will report to Assistant Professor Choo Pei Ling. Major Challenges Some parts of the work (e.g. data collection, analysis) may be held at external sites (e.g. NUS, NTU) which may require swab testing prior to entry. Candidate can expect to work at external sites and SIT Punggol campus as needed. Candidate can expect to be flexible with work hours (i.e. there may be occasions where evening or weekend work is needed).

Role Overview As a University of Applied Learning, Singapore Institute of Technology (SIT) works closely with industry partners to develop applied research capabilities that translate directly into real-world deployment. Our research staff are equipped with industry-relevant skills through hands-on work on operational research platforms and systems. The SIT Centre for Intelligent Robotics (CIR) is a strategic capability centre under RoboPrecinct@Punggol Digital District (PDD), responsible for developing and sustaining the simulation, digital twin, and embodied AI foundations required for precinct-level deployment of robotic systems. We are seeking a Robotics Simulation Engineer to support the Centre’s robotics and embodied AI initiatives. This role focuses on the design, implementation, and maintenance of multi-platform robotics simulation environments, supporting research, development, testing, and Sim2Real transfer of robotic systems. The successful candidate will work across NVIDIA Isaac Sim, Gazebo, MuJoCo, and related simulation platforms, enabling scalable experimentation and robust system-level evaluation prior to real-world deployment. Key Responsibilities Develop, configure, and maintain robotics simulation environments across multiple platforms, including NVIDIA Isaac Sim, Gazebo, MuJoCo, and other relevant simulators for different applications and robotics platform Design modular and reusable simulation assets for robots, environments, sensors, and physics configurations. Integrate robotic platforms into simulation, including URDF/SDF workflows, articulation setup, and validation of kinematics and dynamics. Configure and tune physics simulation parameters (contacts, friction, joint limits, control rates, solver settings) to ensure realistic robot behaviour. Develop and maintain sensor simulation pipelines (RGB, depth, RGB-D, LiDAR, IMU) for perception and learning-based robotics. Support simulation-to-real (Sim2Real) workflows, including domain randomization, sensor noise modelling, and reality-gap mitigation. Integrate simulations with ROS / ROS 2 for closed-loop robot control, testing, and benchmarking. Automate simulation setup, execution, and experiment management using Python and C++, supporting batch experiments and data collection. Support system-level experimentation, benchmarking, and iterative improvement of embodied AI and robotic applications. Prepare technical documentation, simulation guidelines, and reusable templates to support knowledge transfer and long-term maintainability. Provide mentorship to undergraduate students in projects. Carry out risk assessment, and ensure compliance with Work, Safety and Health Regulations. Prepare technical documentation, reports, and implementation guides to support knowledge transfer and long-term maintainability. Contribute to publications and technical notes to disseminate research outcomes. The employee is to communicate with any relevant internal or external stakeholders to ensure project deliverables are met. Any other adhoc duties assigned by supervisor. Requirements Bachelor’s degree or higher in Robotics, Computer Science, Mechanical Engineering, Control Engineering, or a related discipline. Hands-on experience with at least two robotics simulation platforms, such as NVIDIA Isaac Sim, Gazebo, or MuJoCo. Practical experience working with URDF and/or SDF robot models and simulation integration. Working knowledge of robotics physics and dynamics, including articulated systems. Experience integrating simulation environments with ROS or ROS 2. Proficiency in Python, with working knowledge of C++ for simulation or robotics software development. Minimum 2 years of experience in robotics simulation, robotics research, or industry-based robotics projects. Preferred Skillsets (Advantageous) Experience with USD-based workflows and Omniverse-based simulation environments. Familiarity with PhysX and/or other physics engines used in robotics simulation. Experience supporting Sim2Real pipelines for real-robot deployment. Exposure to reinforcement learning, imitation learning, or learning-based control in simulation. Experience working with mobile manipulators, humanoid robots, or legged robots. Familiarity with GPU-accelerated simulation, performance optimisation, and Linux-based systems.