Global Leadership University

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will 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 an industry funded research project where you will be part of the research team to develop advanced Nuclear Quadrupole Resonance (NQR) scanning technologies. Job Responsibilities Participate in research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met.  Undertake these responsibilities in the project:  i. Conduct comprehensive literature review on NQR technology, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system architecture, component selection, and experimental setup. iii. Conduct lab and site tests to evaluate the performance of NQR prototype. iv. Generate design documents and test documents at every stage of development until the final prototype. Coordinate procurement and liaison with vendors/suppliers. Carry out Risk Assessment, and ensure compliance with Work, Safety and Health Regulations.  Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job Requirements Has a bachelor’s degree in Electronics; Specialization in RF/Microwave or Digital Signal Processing (DSP) is preferred. Has solid knowledge of electronics, including circuit design, PCB layout, and hardware troubleshooting. Has relevant competence in the areas of Radio Frequency design and measurement; Familiarity with high-power amplifier systems is a plus. Has relevant experience in the areas of Digital Signal Processing (DSP) design and implementation is a plus. Experience in electromagnetic simulation using CST Studio Suite and circuit simulation with PSpice, LTspice, or equivalent is an advantage. Experience with developing software for instrument control and data acquisition using LabVIEW is helpful. Key Competencies Strong experimental skills and theoretical knowledge in electronic circuits, and RF or DSP design, with the ability to apply hands-on prototyping and system development. Able to build and maintain strong working relationships with people within and external to the university. Self-directed learner who believes in continuous learning and development Proficient in technical writing and presentation  Possess strong analytical and critical thinking skills Show strong initiative and take ownership of work

Introduction As a University of Applied Learning, the Singapore Institute of Technology (SIT) collaborates actively with industry in our research work. We seek a motivated candidate who is keen on rigorous research, the practical applications of code exfiltration and reverse engineering as well as security penetration testing. The primary responsibility of this role is to deliver on an industry innovation research project where you will be part of a research team to investigate and determine potentially critical Commercial Off-The-Shelf (COTS) drone system vulnerabilities impacting confidentiality, integrity and availability. Key Responsibilities Participate in the research project with Principal Investigator (PI), Co-PIs and research team members to ensure all project deliverables are met. Initiate and help guide work and demonstrations in the following tasks: Firmware extraction and reverse engineering. Study, understand and analyse mobile platform applications. Design, develop and test AI-driven security testing techniques. Design, develop and test control augmentation techniques. Adaptation of developed techniques for more generic use. Perform project documentation and technical mentoring of student assistants. Carry out Risk Assessment, and ensure compliance with Work, Safety and Health Regulations. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment.  Job Requirements A good degree in Computer Science / Engineering, preferably with a relevant postgraduate qualification for Research Fellow. Proficiency with firmware extraction and reverse engineering. Proficiency with penetration testing techniques and tools. Proficiency with software scripting and development work on desktop and / or mobile platforms. Key Competencies Self-directed learner who believes in continuous learning and development. Possess strong analytical and critical thinking skills. Show strong initiative and take ownership of work. Proficient in technical writing and presentation. Able to build and maintain good working relationships with people within and external to the university.

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will 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 Research Engineer will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap camera footage for wildlife monitoring and conservation efforts. Job Responsibilities Participate in and manage the research project together with the PI, Co-PI, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate the models into a working system prototype. Perform data annotation. Conduct experiments, analyse results, and iterate models for improved accuracy and efficiency. Prepare project documentation, technical reports, and academic publications. Collaborate with industry partners and contribute to technology transfer efforts. Support the design of simple web interfaces or dashboards to visualise CV model outputs, working alongside developers when needed. Contribute to system integration by applying familiarity with backend/frontend workflows, ensuring CV models can be accessed through user-facing applications. Assist in deployment of CV solutions on cloud or edge platforms with basic interface support for end-users. The candidate is to liaise and communicate with any internal or external stakeholders to ensure project deliverables are met and to perform any other adhoc duties assigned by Supervisor.  Technical Requirements: Possess strong technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow, Keras) Computer vision models for object detection and classification (e.g., YOLO, R-CNN variants, EfficientNet, ResNet, U-Net) Image processing and computer vision techniques  Python programming and relevant libraries (e.g., OpenCV, NumPy, scikit-learn, Pandas, Matplotlib) Experience with dataset preparation, model training, and performance evaluation Candidates with strong computer vision expertise and proven success in 1-2 substantial CV projects are welcome to apply regardless of domain-specific experience Familiarity with Web/Full-Stack Development: Basic understanding of frontend frameworks (e.g., React, Angular, or Vue.js) Eposure to backend development (e.g., Flask, Django, Node.js) Awareness of RESTful APIs and microservices architecture. General knowledge of database systems (SQL/NoSQL). Experience with cloud platforms (AWS, GCP, Azure) for deployment and scaling Educational Requirements: Hold at least a Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological datasets or wildlife imagery Familiarity with data annotation tools and practices for large-scale datasets Knowledge of model deployment and optimization (e.g., ONNX, TensorRT, model quantization) Experience with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices