Are you a medical student curious about how technology and mathematics are transforming the world of MedTech? The iBSc Mathematics, Computers and Medicine at UCL offers a unique opportunity to explore this intersection and address real-world biomedical challenges. Whether you’re a complete beginner or already have some experience, this programme is designed to take you from basic knowledge to becoming a confident researcher. In this blog, I’ll share my personal experience with the course and explain why this innovative intercalation is a game-changer for anyone passionate about the future of healthcare and technology.
🤔 What Do You Do During the Year?
The programme includes five compulsory modules and one optional module of your choice. Let me walk you through the compulsory modules.
💻Introductory Programming (Term 1)
This module serves as a beginner-friendly introduction to Python programming, making it accessible even to those with no prior experience. It covers essential programming concepts such as loops, if/else statements, lists, file handling, and object-oriented programming (including classes, objects, and inheritance). Through a combination of lectures, lab tasks, and tutor support, the module helps students progress rapidly from novices to confident coders within one term.
The standout feature of this module is the group project, which accounts for 80% of the assessment. For example, we worked on building systems like a “Humanitarian Emergency Management System,” combining logical coding with teamwork. This hands-on experience makes learning engaging and equips students with the skills to solve real-world problems.
For me, this module was a game-changer. Starting with no coding experience, I gained practical skills in problem-solving and debugging, which proved invaluable during my dissertation. If you’re looking for a rewarding, interactive way to learn Python, this module delivers both technical skills and critical thinking.
📊Mathematical Methods in Medical Physics (Term 1)
This module is designed to give iBSc students the mathematical foundation needed to excel in advanced courses like Machine Learning in Term 2. It ensures a level playing field with third- and fourth-year physics and electrical engineering students.
The pure mathematics component covers topics such as coordinate geometry, vectors, differentiation, integration, complex numbers, Fourier transforms, differential equations, and matrices. In statistics, students delve into distributions (binomial, Poisson, Gaussian), hypothesis testing, chi-squared tests, ANOVA, and Bayes’ theorem.
While the module can feel overwhelming at first—especially for those who haven’t studied maths recently—it starts with basic concepts and builds up to more advanced topics like matrices and calculus. The emphasis on problem-solving and practical applications, rather than rote memorisation, makes it both challenging and rewarding. By the end of the term, you’ll not only understand the theory but also develop practical skills that prepare you for advanced machine learning concepts and their applications in dissertation work.
🩺Mathematical Modelling in Biomedicine (Term 2)
This module introduces students to mathematical models in biomedicine, combining coursework with seminars led by UCL researchers. It explores how mathematical models apply across various scales and contexts, from molecular structures and brain processes to epidemiology and evolutionary studies.
Students learn core modelling techniques, including deterministic, stochastic, mechanistic, and statistical approaches. The focus is on applying existing models to biological scenarios (e.g., bacterial colony growth), developing new models, and evaluating their assumptions and limitations.
A key feature of the module is the opportunity to visually present models through diagrams and summarise findings in a poster presentation. This exercise not only sharpens your analytical skills but also provides excellent preparation for future higher medical specialty applications. By translating biological processes into mathematical frameworks, the module enables in-depth analysis, hypothesis testing, and experimentation. It’s particularly relevant for understanding concepts like blood flow dynamics or SEIR models used in predicting disease spread, such as COVID-19. This knowledge is essential for grasping the mathematical principles underlying medical research.
🤖Machine Learning for Domain Specialists (Term 2)
This module delves into the core principles and mathematical foundations of machine learning, combining theoretical understanding with practical data analysis. Topics include supervised learning techniques such as linear regression, decision trees, neural networks, and support vector machines (SVMs), as well as unsupervised methods like PCA, k-means clustering, and Gaussian mixture models.
Widely regarded as the most challenging module in the programme, it is taught at a third-year computer science level. The assessment is divided into a 60% exam and a 40% group coding project. The group project is critical for success, offering a hands-on opportunity to apply machine learning techniques in a collaborative setting.
While the exam is notoriously tough, practising past papers—available from lecturers—proved invaluable, as similar questions often appear. For deeper insights, I highly recommend Andrew Ng’s online CS229 Stanford course, which greatly enhanced my understanding of machine learning fundamentals.
Overall, this module provides a comprehensive foundation in machine learning, equipping you with both the theoretical knowledge and the practical skills needed for research and future work in the field.
📚Research Project in Computational Biomedical Sciences (Term 2)
The research project was, without a doubt, my favourite part of the iBSc. It offered a chance to conduct independent, interdisciplinary research, using computational and mathematical techniques to explore an open question in biomedicine. I chose a computational project (since lab work isn’t my preference), where I used a motion tracker on an ultrasound probe to gather coordinate data in Excel. I then analysed the data using Python, applying mathematical functions and creating graphs to visualise my findings.
One of the best aspects of this project is the shorter word count for the dissertation—around 3,500 words—since your coding and mathematical modelling are also assessed. For anyone who prefers coding or maths to lengthy writing, this structure is ideal. This project sparked a genuine interest in computational research for me, and it also opened doors to collaborate with UCL researchers and labs, offering opportunities for further research, potential publications, and valuable mentorship.
These compulsory modules provide a well-rounded, interdisciplinary education, equipping students with the skills and knowledge to thrive in computational biomedical sciences. Whether you’re coding for the first time or delving into advanced mathematical concepts, this programme offers a unique and rewarding challenge.
🚀Benefits of the Programme
- Foot in the Door to MedTech: This iBSc showcases a strong commitment to MedTech, providing a solid foundation for careers in the industry, research, or master’s programs. It’s highly regarded by MedTech professionals and has given me access to projects I wouldn’t have encountered otherwise.
- From Basics to Intermediate Expertise: The program takes you from the fundamentals to a solid intermediate level, supported by peers, lecturers, and researchers. It’s an excellent stepping stone for advanced studies in mathematics or computer science at the master’s or PhD level.
- A Unique Opportunity: There’s no other iBSc like it, you can get a head start into MedTech whilst being a medical student.
- Access to UCL’s Research Network: Benefit from exclusive access to UCL’s renowned research network, connecting with leading researchers in the field.
- The UCL and London Advantage: Studying at UCL in London is truly inspiring, surrounded by ambitious individuals driving innovation. As the #1 institution for neuroscience research, UCL provides world-class opportunities, while London’s vibrant energy adds unmatched experiences.
- Building Confidence and Practical Skills: I never thought I could code or handle university-level maths, but this iBSc empowered me to do both, equipping me with valuable real-world skills and newfound confidence.
- Workload: This iBSc isn’t a break from medicine—it’s challenging and requires effort. However, the course layout makes it manageable, with lighter and heavier modules balanced each term. The dissertation, worth 37.5% of your final grade, puts you in control of your success, motivating passion and dedication to your research. While tough, the experience is incredibly rewarding, especially for medics with no prior tech background.
- Imposter Syndrome: Transitioning from medicine to university-level maths and computing can feel daunting, but the steep learning curve enables you to achieve remarkable progress in just one year.
💼Potential Career Pathways:
👩💻 MedTech Industry Roles
- Biomedical Data Scientist: Analyse and interpret medical data to develop solutions for patient care and research challenges.
- Medical Software Developer: Design and build software for medical devices or applications such as electronic health records.
- Healthcare AI Specialist: Develop and implement artificial intelligence models for diagnostics, patient monitoring, and operational improvements.
🔬Academic and Research Positions
- Computational Biomedicine Researcher: Engage in advanced research on biological modelling or machine learning applications in medicine.
- PhD Candidate in Medical Technology: Utilise the skills gained in the iBSc to undertake groundbreaking research and secure funding for innovative projects.
- You can also pursue a master’s degree in related fields such as Bioinformatics, Health Data Science, or Artificial Intelligence for Medicine.
📜Consulting and Policy
- Healthcare Technology Consultant: Provide strategic advice to healthcare providers and MedTech companies on adopting cutting-edge technologies.
- Health Policy Analyst: Leverage data-driven insights to influence policies and initiatives centred on digital health and medical innovation.
- Launch your own venture aimed at developing innovative medical devices, AI-powered diagnostic tools, or patient-focused applications.
📋 How to Apply
- ‘A’ grade at A-Level Maths.
- A personal statement.
- A full academic transcript from your Medical School showing at least two years of study.
- A reference from a tutor or lecturer who can vouch for your academic abilities, submitted via the official Reference Form.
- An official letter from your home institution confirming your release for the specified study period. This letter must be sent directly from your institution or provided as a stamped and signed copy.
- A completed form for Disability and Ethnic Origin Monitoring.
Applications for 2025 entry will open in early February. The application period for external candidates will close in late March 2025.
📄Tips for a Strong iBSc Application
- Clearly articulate why you’re interested in pursuing the iBSc. Highlight any relevant experiences and explain what you hope to gain from the programme. Share specific aspects of mathematics or computing in medicine that excite you.
- Mention any books, articles, or videos you’ve engaged with, as well as any coding projects or research topics that have captured your interest.
- Describe how these experiences have shaped your perspective and how you envision contributing to or conducting research at UCL.
🌟Conclusion: Embrace the Challenge
Looking back, the iBSc in Computational Biomedical Sciences was one of the most rewarding experiences of my academic journey. It pushed me to develop skills I never thought possible—coding, complex mathematics, and research. While the programme can feel daunting, especially if you come from a medical background with no tech experience, it’s designed to guide you from the basics to advanced concepts, building your confidence along the way.
If you’re passionate about using technology to transform medicine, this iBSc offers a unique and exciting opportunity. It will equip you with the tools, knowledge, and resilience to bridge the gap between medicine and technology, empowering you to make meaningful contributions to the future of healthcare.