Escola Bahiana de Medicina e Saúde Pública

Research Assistant Health and Social Sciences Cluster Singapore Institute of Technology We are seeking a motivated and capable Research Assistant to join an exciting translational research project on spinal sarcopenia in older adults, with a focus on muscle degeneration, senescent progenitors, and regenerative failure. This role offers a unique opportunity to work across clinical research, functional assessment, imaging analysis, and laboratory investigation in a multidisciplinary academic environment. The successful candidate will support a funded research project that aims to generate clinically relevant insights into ageing, muscle health, and spine-related conditions. This is an excellent opportunity for candidates who are passionate about meaningful research and wish to build strong experience in applied human and translational studies. Key Responsibilities Support the Principal Investigator and research team in the implementation and coordination of the project Assist with participant recruitment, scheduling, follow-up, and study administration in accordance with approved ethics protocols Conduct and document functional assessments and study-related data collection Retrieve, organise, and manage imaging data, and assist with image-based analysis Support laboratory workflows such as tissue handling, histology, immunofluorescence, qPCR, ELISA, and related analyses Maintain accurate databases, study records, and project documentation Assist with literature review, data cleaning, preliminary analysis preparation, reporting, and regulatory submissions Coordinate procurement of reagents, consumables, and research-related items Ensure compliance with laboratory safety, biosafety, and institutional research governance requirements Job Requirements Bachelor’s degree in biomedical science, life sciences, physiotherapy, diagnostic radiography, medical laboratory science, or a related discipline Strong interest in translational, musculoskeletal, ageing, or clinical research Experience in clinical research coordination, patient-facing data collection, imaging analysis, or laboratory techniques will be advantageous Familiarity with histology, microscopy, immunofluorescence, molecular assays, or tissue-based workflows is preferred Good written and verbal communication skills Strong organisational skills, attention to detail, and professionalism Proficiency in Microsoft Office; familiarity with ImageJ, SPSS, STATA, or equivalent tools is an advantage Key Attributes Motivated, responsible, and able to work independently Careful and accurate in handling data, samples, and documentation Strong team player who can work effectively with academic, clinical, and student collaborators Willingness to learn and adapt in a dynamic multidisciplinary research environment Why Join Us This role offers valuable exposure to clinically relevant translational research with opportunities to develop experience in human studies, imaging, laboratory science, and multidisciplinary collaboration. The successful candidate will contribute to a meaningful project with real-world healthcare relevance while building a strong foundation for future research or postgraduate opportunities. Contract Type 1-year contract Work Location Singapore Institute of Technology, with project activities at SIT and partner sites as required.

Join a unique British University in China. The University of Nottingham Ningbo China (UNNC) was the first Sino-foreign university to open its doors in China. This award winning campus offering a UK style education has grown to establish a student body of over 10,000 in just 21 years. It is looking for ambitious, talented academics with a passion for teaching as well as research flair to join its team of science and engineering experts. UNNC is part of the University of Nottingham’s Global University, and offers unique teaching and research opportunities in a highly dynamic economy. Applications are invited for the above post in the Electrical and Electronic Engineering (EEE) Department. The department delivers an undergraduate programme that is jointly accredited across the Nottingham campuses in the United Kingdom, Malaysia and China. It is expected that the successful candidate will support excellence in teaching and research in the Department and across the three campuses, while developing an internationally recognised research programme. The successful candidate will be expected to make a significant leadership impact within their academic unit and in the research and teaching area of magnetic resonance imaging (MRI). The role holder will be expected to contribute to teaching and curriculum development for undergraduate and postgraduate programmes in the Department of Electrical and Electronic Engineering, to play a lead role in collaborative research projects and conduct ongoing original research and publications in the areas of Magnetic Resonance Imaging (MRI). This post also be linked to research activities linked to the University’s research platforms such as the UNNC Sir Peter Mansfield MRI Centre. Candidates must have a PhD degree in the related field, sufficient research experience with evidence of publication record, and the evidence of demonstrated success in delivering teaching within an agreed quality framework. Excellent English oral, written, and listening skills, ability to manage resources and an understanding of management processes and ability to build relationships and collaborate with others, internally and externally are also essential. Salary will be within the range of 635,038 RMB to 780,622 RMB (Associate Professor) or RMB 450,190 to RMB 592,815 (Assistant Professor) per annum, depending on skills and experience depending on qualifications and experience (salary progression beyond this is subject to performance). In addition, an attractive package including employment support allowance, schooling and insurance will be provided for international appointment. The post will initially be offered on a fixed term contract with the University of Nottingham Ningbo China for a period of up to 5 years with effect from August 2026 or thereafter as soon as possible. This contract may be extended by mutual agreement, subject to revised terms and conditions. All applicants are required to formally apply online https://jobs.nottingham.edu.cn/job/184534/ by 23:59 Beijing Time, 8 May 2026, which should include the following: a cover letter an up-to-date CV a publication list A statement of teaching philosophy giving evidence of teaching excellence A research statement indicating your planned research activities as part of our department Informal enquiries may be addressed to: Dr C.F. Kwong, Department Head of Electrical and Electronic Engineering, email: chiew-foong.kwong@nottingham.edu.cn. Please note that applications sent directly to this email address will not be accepted. Interviews will take place in Ningbo, China, if feasible – virtual interviews may also held. Please be advised that normally your references will be contacted prior to the interview. If you are unable to apply on-line please contact the Human Resources Department, Tel: 86 574 8818 000 (Ext. 8404), Email: Job@nottingham.edu.cn.

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