California State University, San Bernardino

Are you a recent postgraduate ready to go wild and stand head and shoulders above the crowd, using advanced AI and computer vision to shape the future of animal welfare? The University of Surrey, in collaboration with Marwell Wildlife - a leading conservation charity and the owner-operator of Marwell Zoo - is recruiting a forward-thinking AI and Computer Vision Associate to lead a transformative Knowledge Transfer Partnership (KTP) Project.  This pioneering project will develop an AI-powered nocturnal behaviour observation system, transforming how animal welfare is monitored and understood. By uncovering night-time behavioural changes, you will help enable earlier detection and proactive prevention of health and welfare issues before these become serious. This cutting-edge work will help redefine how animal health and wellbeing are protected and improved.  This growing partnership between the University and Marwell Wildlife aims to connect people with nature, lead the charge in caring for the natural world, and support a thriving future for wildlife. The Role Based at Marwell Zoo (near Winchester, Hampshire), you will lead the design and delivery of an end-to-end, 24/7 animal welfare monitoring system. Your work will advance welfare science and support innovative, data-driven revenue streams that support Marwell’s long-term sustainability. You will work at the intersection of: Artificial Intelligence & Computer Vision Animal Behaviour & Welfare Science Data Analytics, Visualisation & Business Insight You will be embedded within Marwell’s Animal Science team and supported by University of Surrey academics: Professor Kevin Wells, Professor of AI in Human and Veterinary Healthcare.  Dr Marco Volino, Lecturer in Computer Vision and Graphics. KTPs offer a unique springboard for your career, combining academic rigour, industry experience, and leadership responsibility to support your development as a future leader in this field.  About You You are ambitious, curious, and impact-driven, with an interest in animal welfare, animal behaviour, or conservation. You will hold a postgraduate degree in AI, Data Science, Computer Vision or a closely related discipline with strong technical component, or equivalent industrial experience. You will bring experience in: Computer vision and machine learning: developing, training, evaluating, and deploying computer vision models. Data visualisation and analytics: turning complex data into actionable insights. Programming: strong skills in languages such as Python and C#. Applied data science: solving real-world challenges using Machine Learning and Artificial Intelligence techniques. Business Intelligence and Data Visualisation: using tools like Power BI, SQL (ideally PostGIS), and cloud-based databases.  Documentation and communication: clearly documenting processes, data models, and reporting structures. Project management and business acumen: mapping business processes, presenting insights, and translating technical concepts for diverse audiences.  You thrive in collaborative environments, are keen to learn new technologies and are motivated by driving innovation through AI and data-driven solutions. This 36-month fixed-term KTP Associate role offers: Hands-on, industry-based experience leading a high-impact AI project in a live conservation-focused setting, working closely with animal science experts Mentorship from leading academics, industry experts, and a dedicated KTP support team. A Generous personal development budget and dedicated time for training and development. Potential for a permanent role at Marwell Wildlife upon successful completion of the KTP (subject to performance and business needs). This is a rare opportunity to build a cutting-edge technology solution from the ground up while directly contributing to animal welfare and the future resilience of conservation-focused organisations. How to Apply Please submit your CV and cover letter on the University website. Informal enquiries are welcomed and can be directed to Dan Bance at: ilovektp@surrey.ac.uk. Please note applications sent directly to this email address cannot be accepted. As part of the Knowledge Transfer Partnership (KTP) Programme, your application will be reviewed by representatives from both the University of Surrey and the host organisation, the Business Partner. By submitting your application, you consent to your personal details being shared with the business partner and the funder for the purposes of recruitment and project delivery. Please note that we reserve the right to close this vacancy ahead of the advertised deadline if we receive a high volume of strong applications. 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 Fellow / Engineer (Building-Grid Interaction Design) will contribute to a two-year applied research project funded by the BCA, focusing on the development of a power grid interactive building or cluster of buildings. The role involves designing and implementing monitoring, visualization and control system to coordinate multiple energy sources including battery energy storage systems (BESS), solar PV, and diesel generators, and dynamically interacting with building management system to shed signficant loads under varying operating conditions. The research staff will develop, simulate, and test controller logic using MATLAB/Simulink or similar environments, analyse performance across diverse scenarios, and document results for technical reporting. This position is ideal for candidates with a strong foundation in modern facilities management, mechanical and electrical engineering and energy efficiency management.  Key Responsibilities Participate in the research project to design a microgrid controller for grid interactive building applications. Develop control algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with the microgrid model and validate performance under different simulated conditions. Conduct testing of control strategies under various operating conditions such as extreme weather, load variation, equipment failure and deployment constraints. Prepare documentation, flowcharts, control logic diagrams, and controller specifications for the final report. Assist with procurement of software licences and technical equipment. Conduct Risk Assessments and ensure compliance with WSH regulations. Support supervision of capstone project students involved in the project. Work independently and collaboratively to maintain research software and project tools. To communicate and liaise with any internal and external stakeholders to ensure project deliverables are met. Any other ad-hoc duties assigned by supervisor. Job Requirements Bachelor’s degree or above in Electrical Engineering, Computer Engineering, or a related field from a recognised university. Strong programming skills in MATLAB/Simulink, Python, C/C++, or similar environments. Understanding of control systems, power electronics, or energy management strategies. Experience in microgrid controller design is advantageous. Familiarity with BESS control strategies, SOC management, or optimisation algorithms preferred. Support supervision of capstone project students involved in the project. Ability to implement and test algorithmic logic using simulation tools. Strong analytical, problem-solving, and debugging skills. Excellent technical writing, communication, and interpersonal 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 skills that are relevant to industry demands while working on research projects in SIT.  About the DIGNIFIED programme The DIGNIFIED programme is dedicated to developing elderly-friendly, textured modified foods with desirable organoleptic and nutritional properties. Our mission is to enhance the quality of life for elderly individuals by creating innovative, nutritious, and visually appealing food products that cater to their specific dietary needs. Job Description:  We are seeking a highly motivated Research Engineer to join our team for a fixed-term project. The successful candidate will be responsible for developing and validating objective texture testing methods that can accurately categorize foods within the International Dysphagia Diet Standardisation Initiative (IDDSI) framework, with a specific focus on Level 5 (Minced & Moist). This role will involve designing custom test fixtures, conducting practical laboratory work, and performing data analysis to create a robust and reliable testing protocol. Key Responsbilities: Conduct a comprehensive literature review on food rheology, texture analysis, particle analysis, image analysis and the IDDSI framework. Design and execute experiments to develop new instrumental texture analysis methods suitable for IDDSI Level 5 foods. Validate new methods against existing standards and a sensory panel to ensure accuracy and clinical relevance. Collect, analyze, and interpret data from instrumental and sensory evaluations. Document all experimental procedures, findings, and analyses in technical reports and prepare presentations to industry stakeholders and manuscripts for publication in peer-reviewed journals. Collaborate with a research team, including food scientists, clinicians, and engineers, to ensure the developed methods are practical and clinically relevant. Job Requirements: A degree in Food Science, Food Engineering, Materials Science, or a related field. A Master's or Ph.D. in a related research field is advantageous. Experience with instrumental texture, image analysis and particle sizing techniques (e.g., texture analyzers, universal testing machines) is highly desirable. Strong analytical and problem-solving skills. Proficiency in material characterization techniques, such as stress-strain characterization. Proficiency in analysis software (e.g., Microsoft Excel, R, or other statistical software). Excellent written and verbal communication skills. Ability to work independently and as part of a team. Demonstrated commitment to strong research ethics and data integrity