Avicenna Tajik State Medical University

Organisation: Cranfield University Faculty or Department: IT Services Based at: Cranfield Campus, Cranfield, Bedfordshire Hours of work: 37 hours per week, normally worked Monday to Friday. Flexible working will be considered. Contract type: Permanent Salary: Full time starting salary is normally in the range of £37,897 to £42,100 per annum Apply by: 03/05/2026 We are seeking a motivated and enthusiastic individual to join our Student Systems Team. About the Role In this exciting role within our Student Systems Team, you will use analysis techniques and develop functionality to meet new business requirements, including automating business processes, establishing requirements, developing and implementing new functionality, system integrations, implementing new standard components, as well as communicating complex concepts to a wide variety of stakeholders. About You The ideal candidate will have a degree-level qualification in IT, Software Engineering or similar, and have experience in developing the Tribal SITS:Vision student records system, and ideally Maytas apprenticeship system. You will have overall experience in software development lifecycle management and complex relational database design, preferably within the HE sector. You will possess an excellent mix of interpersonal communication and tactical thinking skills, combined with a detailed knowledge of Tribal SITS, SQL, CSS, HTML v5, JavaScript and JQuery. You will be expected to engage with and influence staff across the University to achieve collective business requirements from key stakeholders for translating into prioritised deliverable systems development plans. You will possess strong system development skills, demonstrating a flexible, enthusiastic and positive attitude. About Us As a specialist postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here. IT Services is a University-wide Professional Service Unit (PSU) with the remit to work in partnership with the University’s Faculties and other PSUs to provide a full-service digital IT systems and information management capability in support of the strategic priorities for Learning, Research and Administration, as detailed in the University’s Corporate Plan. Our Values and Commitments Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here. We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. We are a Disability Confident Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity Working Families. Find out more about our key commitments to Equality, Diversity and Inclusion and Flexible Working here. Working Arrangements Collaborating and connecting are integral to so much of what we do. Our Working Arrangements Framework provides many staff with the opportunity to flexibly combine on-site and remote working, where job roles allow, balancing the needs of our community of staff, students, clients and partners. How to apply For an informal discussion about this opportunity, please contact Claire Gale, Head of Student Systems, on (T): 01234 754139 or (E): c.a.gale@cranfield.ac.uk Please do not hesitate to contact us for further details on E: peoplerecruitment@cranfield.ac.uk. Please quote reference number 5283. Closing date for receipt of applications: 3 May 2026

The University of Surrey is a global community of ideas and people, dedicated to life-changing education and research. We are ambitious and have a bold vision of what we want to achieve - shaping ourselves into one of the best universities in the world, which we are achieving through the talents and endeavour of every employee. Our culture empowers people to achieve this aim and to collectively, and individually, make a real difference. The role We are looking to recruit a skilled Laboratory Technician, who will operate under the direction of the Research Laboratory Manager in the discipline of Nutrition, Exercise Science, Chronobiology and Sleep to work in the sections of Nutrition, Food, Exercise Science and Cardiovascular Science. This exciting opportunity has become available to work alongside our academics, involving the analysis of clinical samples from nutritional and metabolic studies and supporting wet lab project students within the different sections. As such, we are looking for a highly motivated Technician with a key scientific interest in nutrition, food science and cardiovascular science to join the team. This position would suit someone with excellent attention to detail, wet lab/project experience, and an interest in food, nutrition and cardiovascular research and development. Previous experience with microbiological assays, biochemical assays, or analytical techniques in food science is essential for the successful applicant, although further training will be provided. In addition to further developing existing knowledge/skills in nutritional and metabolic assays, this position will provide opportunities to work alongside an experienced team of Laboratory Technicians from a range of backgrounds, attend research seminars, and build professional contacts across the Faculty of Health & Medical Sciences. What we can offer In addition to a competitive salary you will receive 25 days annual leave, with additional days for Bank Holidays and University closure days. We offer a generous pension, flexible working options, access to world-class leisure facilities, a range of travel schemes and supportive family friendly benefits including an excellent on-site nursery. Further information For further information, please see the below job description.  For an informal discussion, please contact Anuska Mann – Research Laboratory Manager (Nutrition), a.mann@surrey.ac.uk. To apply please upload your CV. You will also be asked some brief questions to help us understand your suitability for the position. Interviews will be held on campus on Thursday 7th May. The University of Surrey is committed to providing an inclusive environment that offers equal opportunities for all. We value everyone in our community and are seeking to increase the diversity. Therefore, we particularly encourage applications from under-represented groups, such as people from Black, Asian and minority ethnic groups and people with disabilities. Further details Job Description

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