Projects   

Our cohort of students and professionals undertaking short projects
supported by the project are listed below, followed by a selection of
reflective case studies.

Completed Projects Summary

2024/2025 Cohort: Selected Case studies

Course: Masters in Artificial Intelligence  

Partner: The University of Manchester 

Title: AI support for statistical disclosure checks and synthetic data 

Biography: I completed my undergraduate degree in AI and Machine Learning at BNM Institute of Technology, co-authoring “Face Recognition using MTCNN, Inception-ResNet with Ensemble Approach”, which proposed a deep learning model combining advanced face detection with ensemble classification.  My final-year project, SurgiLearnVR, a VR medical training platform, won the Best Project Award.  

My journey includes internships with Oracle Financial Services Software and PwC, applying AI, machine learning, and cloud technologies to improve systems. These experiences sharpened my technical expertise while giving valuable insights into how AI can enhance efficiency and trust in both healthcare and financial systems. 

I am passionate about mentorship and outreach, supporting diversity in tech initiatives, and accessibility in AI. I envision a future where AI is not only powerful but also ethical, inclusive, and accessible, and am committed to contributing to that vision. 

Project Summary: My project focused on creating an AI agent that can generate synthetic healthcare data, motivated by the challenge of accessing real patient data for research, which is often restricted due to privacy concerns. By producing realistic but entirely fictional medical records, this offers researchers a safe way to test ideas, build models, and explore healthcare questions without exposing sensitive information. 

I combined advanced AI language models with smart data design so the agent could respond to simple prompts like “generate 50 asthma patients with their prescribed medications.” The generated datasets were then carefully compared with real ones to ensure they looked and behaved realistically while still protecting privacy. I found that AI is especially effective at capturing important clinical data e.g. age, gender, and conditions, though some hospital-related details were less precise. 

The research demonstrated that AI-driven synthetic data is a powerful tool for healthcare innovation, offering a balance between usefulness and privacy. It opens the door for safer, faster research and highlights how AI can be applied responsibly in sensitive fields like medicine. 

Reflections on SDE Skillshub Project Support: The supervision and the support from the Skills Hub team created an environment that was both academically rigorous and practically insightful fostering independence and growth. My supervisor provided in-depth technical mentorship and strategic direction, while the wider SkillsHub team complemented this with expertise in governance, context, and reflective practice. Their combined guidance provided relevance to real-world applications and implications such as ethics, IG, and applicability. Supervision struck a balance between academic rigor and approachable mentorship and was consistently clear, constructive, intellectually stimulating, and encouraging.  

Regular feedback sessions addressed challenges early and maintained progress. More structured checkpoints around the later evaluation phases and guidance on benchmarking against similar synthetic data initiatives would have further contextualized the work. 

Working within this supportive framework was enriching and enabled me to develop a stronger understanding of how technical innovation must align with wider factors. The process helped me build new skills in communicating complex ideas across technical and non-technical settings and greater confidence in applying my knowledge in practical, interdisciplinary contexts. These insights will be invaluable for future technical roles within SDEs.

Name: Nouar Nagem 

Course: MSc Artificial Intelligence  

Partner: The University of Manchester 

Project Title: AI support for statistical disclosure checks and synthetic data 

Biography: I recently completed both a BSc and an MSc in Artificial Intelligence, building strong skills in programming, machine learning, and data engineering along the way. I enjoy learning about technology and exploring how AI can be applied in different fields, especially healthcare. Outside my studies, I developed skills in data analysis, project coordination, and stakeholder collaboration, strengthening both my technical and organisational abilities. 

Project summary: I developed an AI agent that understands natural language queries and generates synthetic healthcare data. It is designed to be capable of handling queries from uploaded datasets; supporting medical synonyms and abbreviations using Pinecone with OpenAI embeddings and NHS SNOMED terms; and of producing fully fictional, privacy-preserving patient records with the OpenAI API. 

Reflections on SDE Skillshub Project Support: the supervision form the SDE Team Development Hub was supportive, with weekly meetings providing guidance while allowing independent work. This project gave me hands-on experience developing a complex AI agent, interpreting natural language queries, and generating structured synthetic data. It strengthened my problem-solving skills, improved how I communicate complex ideas, and increased my confidence in pursuing a career in technical development of IT infrastructure. 

Name: Emaan Hajara 

Course: MSc Health Data Science 

Institution: The University of Manchester 

Project Title: Designing a secure architecture that blends on-premises and cloud based computation infrastructure for use in SDE 

Biography: I studied for my undergraduate degree in Biological Sciences (Biotechnology with Enterprise) at the University of Leeds.  I found that along the way, I realised I wanted to move into a more technical direction. This led me to pursue an MSc in Health Data Science at the University of Manchester. Here, I was introduced to the principles and approach of cloud engineering through my dissertation. That project revealed just how exciting and impactful technical infrastructure can be. I am now eager to explore roles that combine these skills and have a real-world impact in cloud infrastructure, DevOps, and data-driven research 

Project Summary: For my dissertation, I built a secure hybrid cloud architecture from scratch, linking on-premises resources with Azure to create a scalable, automated, and compliant environment for sensitive data. Having started with no prior experience in cloud engineering, I successfully designed and tested a reproducible blueprint demonstrating that hybrid models can be secure and cost-effective, and that it’s possible to modernise infrastructure without compromising on governance requirements. 

Reflections on SDE Skillshub Project Support: My supervisors were invaluable in helping me make this leap into technical development. They guided me through unfamiliar tools like Terraform, GitHub Actions, and Visual Studio Code, helping me work through challenges, rather than just giving instructions, which in turn made me much more confident in applying the skills myself. With regular meetings and feedback, I was not only able to keep on track but was also presented with multiple opportunities to improve, whether that was my skills or the dissertation itself. Their input undoubtedly strengthened the outcome of the project. 

With hands-on expertise in hybrid cloud design, CI/CD pipelines and compliance frameworks, I now feel much more confident and ready for roles in DevOps and cloud engineering and am more excited than ever to get started and apply these skills in real-world settings. 

Name: Justin Leung

Job Title: Trainee Data Infrastructure engineer 

Institution: University of Manchester

Project Title: GPU Platform and Hybrid Cloud Deployment with ARO

Biography: I am a recent Computer Science graduate currently working as a Trainee Data Infrastructure Engineer at the University of Manchester. My role involves learning and supporting the development of secure, cloud-based environments such as TREs, with a focus on automation, IaC, and DevOps practices.

So far, I have engaged in a wide range of activities including attending the 2025 TRE Community Conference, completing network and cloud infrastructure training, and contributing to internal infrastructure tasks like configuring GitHub Actions and implementing open-source license scanning using FOSSA. I have also written reports on SDE frameworks and participated in team discussions on projects such as eLab and NCRI and upcoming ARO GPU hybrid cloud deployment. 

Previously, I completed a yearlong placement as a Research Software Engineer, where I contributed to software solutions across academic projects. For my final year university project, I developed a brain tumour segmentation model using Weakly Supervised Learning combining medical imagining with machine learning techniques. 

Project Summary: The ARO project aims to design and implement a secure, scalable hybrid cloud infrastructure to support GPU intensive research workflows. This infrastructure will integrate on premise and cloud-based components to meet the computational demands for enabling data processing, AI model training, and secure analytics within a TRE framework. 

As part of the data infrastructure team, my role involves contributing to architecture design, investigating automation tools like Terraform, ensuring secure access and resource management, and aligning the deployment with SDE principles such as isolation, encryption, and auditability. The project also supports broader goals of interoperability, federation readiness, and compliance with national data governance standards. 

Reflections on SDE Skillshub Project Support: Over the past few weeks, I have gained confidence working with tools like Terraform, GitHub Actions, and Visual Studio Code. My line manager and colleges have been incredibly supportive, guiding me through new concepts, helping me troubleshoot, and encouraging me to solve problems independently. Thie feedback and regular check ins have helped me develop both technically and professionally. 

While I still feel there is a lot to learn, particularly in areas like networking and cloud infrastructure, the mix of technical training, meetings, and exposure to real project requirements has given me a clearer sense of direction. I am looking forward to contributing more to the ARO project, and continuing to build the skills needed for future roles in DevOps and cloud engineering. 

How Trainee scheme will help achieve your career objectives: The trainee scheme provides a structured and supportive environment that aligns well with my career aspirations in cloud engineering with DevOps. Through formal training, I am gaining foundational knowledge in cloud platforms, IaC, networking, and automation. Regular mentoring from my line manager and colleges offers valuable guidance and practical advice, helping me apply what I have learned in real world settings. 

Exposure to key technologies such as CI/CD pipelines, hybrid cloud deployments and SDEs is helping me develop hands on experience with the tools and frameworks I aim to use in my future roles. Additionally, participation in cross team meeting, code reviews, and conferences like the TRE community conference has deepened my understanding of the wider data infrastructure landscape. 

Overall, the scheme is building both my technical capability and professional confidence, setting a strong foundation for my long-term career goals in secure and scalable research infrastructure. 

Name: Saurav Sudhar

Institution: Lancashire Teaching Hospitals

Project Title: Karectl

Biography: I have always been interested in using technology to improve healthcare and health systems. During my undergraduate studies, I developed a deep learning model for diabetic retinopathy detection, which strengthened this interest and introduced me to the real-world potential of AI in medical applications. I went on to complete an MSc in Data Science and Analytics, where projects such as industrial accident report analysis using natural language processing further developed my skills in applying data-driven methods to complex problems. I am particularly motivated by the use of open-source software to build scalable, transparent, and impactful solutions for healthcare. In addition, my experience as a software engineer developing web applications has enhanced my practical skills in programming, systems design, and collaborative development.

Project Summary: I am working on Karectl, a SATRE-compliant Trusted Research Environment (TRE) implementation on Kubernetes, developed as part of the DARE-UK initiative. As a software/infrastructure engineer, I am part of the team building both the TRE infrastructure and the applications within it. The project focuses on developing secure, open-source, and reproducible infrastructure to support collaborative research. Its goal is to make the deployment and maintenance of TREs more efficient, scalable, and community-driven, enabling researchers to access and analyse data safely and responsibly.

Career: The RTP trainee scheme has given me the opportunity to work closely with a team of engineers who have strong technical knowledge and real-world experience in building and maintaining secure infrastructures. This has allowed me to learn how the tools and technologies we use such as Kubernetes, Terraform, Azure etc are applied in practice, which is far more valuable than learning them in theory alone.

Through the scheme, I am also encouraged to take part in training courses,conferences and professional certifications, which is helping me to build a stronger technical foundation and a clearer career direction. The guidance and mentorship from experienced colleagues have been invaluable, giving me insight into best practices and industry standards.

Overall, the scheme is helping me develop both the technical and professional skills I need to progress in my career as a cloud and infrastructure engineer, with a focus on open-source technologies and secure, scalable systems for healthcare research.