Eindhoven University of Technology

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 innovation research project where you will be part of the research team to develop a demonstration scale low pressure membrane system in collaboration with Atera Water Pte Ltd. Key Responsibilities Applicants are invited for a Research Engineer in low pressure membrane system (LPMS). Reporting to the Project Principal Investigator (PI), you will assist in all administration matters including overseeing, planning, forecasting, budgeting as well as ensuring the smooth running of the daily operations for the project Assists in co-supervision of final year projects (FYP) or capstone projects students or student helpers under the Project PI. Assist in designing, planning and setting up of a demonstration scale LPMS plant at a site allocated by PUB. Assist in designing, planning and setting up of a lab-scale LPMS plant at a SIT Punggol campus Assist in carrying out of experiment, data logging and analysis of data collected. Work with computational fluid dynamics (CFD) results generated by an expert to correlate with experimental data Assists the PI in drafting of reports, conference proceedings and journal articles based on the outcome of the projects. Prepares and shares fortnightly report of results from the computations with the program director and PI. Support the procurement and maintenance of the software/hardware under the charge of the PI Any other adhoc duties assigned. Requirements The appointee should have a strong foundation in fluid dynamics and understanding of fluid (air and water) flow is important. Appointee should also have experience with design of experiments, carrying out experiments and results analysis. Prior experience working with membrane systems will be a plus. A recognised degree from a good University B.Eng or M.Eng in Mechanical Engineering, Aeronautical Engineering, chemical or environmental engineering Strong foundation in Fluid Dyanmics Able to work independently with strong data analytical skills, communication, and interpersonal skills. Proficient in handling large data sets and the ability to analysis and interpret results Experience with internet of things setup and controller design (PLC or embedded computing) Key Competencies Good self-discipline and motivated to deliver Show strong initiative and take ownership of work 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

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

Description The Center for Smart Engineering Materials (CSEM), New York University Abu Dhabi, seeks to recruit a post-doctoral associate to work on the development of high-sensitivity integrated photonics sensors in the Silicon Photonics (SiPh) platform. The successful applicant will drive an exciting project on the development of smart optical sensors for environmental monitoring in the oil & gas industry. Our photonics research lab has set modern tools for the design, testing, and optimization of photonic circuits. It is open for collaboration with researchers with a background in basic research to investigate new optical circuits and materials with specific optical, electrical, and mechanical properties. The specialty of this research project is advanced optoelectronics materials for sensing applications. The candidate will work in a multidisciplinary environment consisting of PhD-level scientists, graduate students, and undergraduate students. Applicants must have a Ph.D. in Electrical Engineering, Physics, or a related field. For consideration, applicants need to submit a cover letter, curriculum vitae with a full publication list, transcript, statement of research interests, and three letters of reference, all in PDF format. If you have any questions, please email Dr. Mahmoud Rasras at mr5098@nyu.edu. The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be accepted immediately and candidates will be considered until the position is filled. Please visit our website at http://nyuad.nyu.edu/en/about/careers/faculty-positions.html for instructions and information on how to apply. About NYU Abu Dhabi https://nyuad.nyu.edu/en/ NYU Abu Dhabi is the first comprehensive liberal arts and research campus in the Middle East to be operated abroad by a major American research university. Times Higher Education ranks NYU among the top 30 universities in the world, making NYU Abu Dhabi the highest-ranked university in the UAE and MENA region. NYU Abu Dhabi has integrated a highly selective undergraduate curriculum across the disciplines with a world center for advanced research and scholarship. The university enables its students in the sciences, engineering, social sciences, humanities, and arts to succeed in an increasingly interdependent world and advance cooperation and progress on humanity’s shared challenges. NYU Abu Dhabi’s high-achieving students have come from over 120 countries and speak over 100 languages. Together, NYU's campuses in New York, Abu Dhabi, and Shanghai form the backbone of a unique global university, giving faculty and students opportunities to experience varied learning environments and immersion in other cultures at one or more of the numerous study-abroad sites NYU maintains on six continents. NYUAD is committed to upholding a culture of non-discrimination, anti-harassment, dignity, and mutual respect; providing equal access and opportunity; and fostering academic excellence in learning, research, and teaching. UAE Nationals are encouraged to apply.