National Autonomous University of Mexico

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

As Singapore’s University for Industry, SIT works closely with industry partners 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 position is to deliver on a research project to develop an End-to-end misinformation and disinformation protection, awareness and mitigation pipeline. We will be recruiting a research engineers or fellows, as part of a wider project, to conduct research on: LLM/VLM, media comparison, AI-assisted explainability. The need is for a real-time system, so efficient coding is important. Key Responsibilities: Participate in and manage the research project with Principal Investigator (PI) Ian McLoughlin, Co-PI Tong Rong and the research team members to ensure all project deliverables are met.  Undertake research within your domain, including keeping track of state-of-the-art work. Develop the code, evaluate and test. Write academic publications. Deliver solution-ready demonstration code. Interact well within the team and the customers. Be self-directed to fulfil the project requirements. Job Requirements: Competence in working with languages such as C along with Python-based coding and AI development. Have a degree in Computer Science/Computer Engineering. Possessing a Master’s or PhD degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of image processing is required. Ability to effectively and efficiently utilise industry-standard Linux-based computers for AI. Experience of authoring good quality academic publications. Manage undergraduate research assistants (if appropriate). Liaise with customers and collaborators in A*STAR and NTU, as well as collaborators in the UK and China. Key Competencies: A self-directed worker who believes in continuous learning and development Proficient in academic writing  Possess good critical thinking skills Show strong initiative and take ownership of work Interest in AI, machine learning, image/audio processing

As Singapore’s University for Industry, SIT works closely with industry partners 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 position is to deliver on a research project to develop an End-to-end misinformation and disinformation protection, awareness and mitigation pipeline. We will be recruiting a research engineers or fellows, as part of a wider project, to conduct research on: Signal processing combined with AI-based modification on speech signals. The need is for a real-time system, so efficient coding is important. Key Responsibilities: Participate in and manage the research project with Principal Investigator (PI) Ian McLoughlin, Co-PI Tong Rong and the research team members to ensure all project deliverables are met.  Undertake research within your domain, including keeping track of state-of-the-art work. Develop the code, evaluate and test. Write academic publications. Deliver solution-ready demonstration code. Interact well within the team and the customers. Be self-directed to fulfil the project requirements. Job Requirements: Competence in working with languages such as C along with AI applications as well as Python-based coding . Have a degree in Computer Science/Computer Engineering. Possessing a Master’s or PhD degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of speech, audio, time-series or signal processing is required. Ability to effectively and efficiently utilise industry-standard Linux-based computers for AI. Experience of authoring good quality academic publications. Manage undergraduate research assistants (if appropriate). Liaise with customers and collaborators in A*STAR and NTU, as well as collaborators in the UK and China. Key Competencies: A self-directed worker who believes in continuous learning and development Proficient in academic writing  Possess good critical thinking skills Show strong initiative and take ownership of work Interest in AI, machine learning, image/audio processing