Artificial intelligence (AI) and machine learning are transforming biomedical research. From interpreting complex omics datasets to exploring the scientific literature and identifying novel biomarkers, researchers are increasingly using AI to answer biological questions more efficiently and uncover new insights.
Yet for many biologists, AI can feel overwhelming. New tools are emerging almost daily, formal training is limited, and many existing courses are designed either for computer scientists or provide generic introductions that don't reflect the realities of biological research.
Developed by the British Society for Immunology (BSI) in partnership with C3Bioscience, this new training programme has been designed specifically for researchers working in immunology. Rather than teaching AI in isolation, you'll learn how to apply it to real research challenges through practical examples, datasets and case studies drawn from modern immunology.
Whether you're taking your first steps into AI or looking to incorporate advanced machine learning into your existing computational workflows, these courses will help you build practical skills that you can apply immediately to your own research.
Why learn AI and machine learning?
AI isn't replacing scientific expertise. It's becoming another tool in the modern research toolkit, helping researchers analyse increasingly complex data, work more efficiently and make better informed decisions throughout the research process.
| Research challenge | How AI can help |
|---|---|
| Build an AI research assistant | Search, compare and synthesise large volumes of scientific literature, helping you stay up to date with new discoveries, prepare grant applications and identify relevant evidence more efficiently. |
| Interpret omics datasets | Support the analysis of transcriptomic, single cell, spatial and other multiomics datasets by providing biological context and identifying meaningful patterns within complex data. |
| Identify novel biomarkers | Apply AI and machine learning approaches to discover biomarkers, classify cells and generate new biological hypotheses from large datasets. |
| Enhance data analysis | Complement existing computational workflows by helping interpret results, explain biological pathways and support biological discovery. |
| Strengthen research writing | Refine grant applications, organise ideas, summarise evidence and communicate complex scientific concepts more efficiently. |
| Streamline routine tasks | Automate repetitive activities, improve coding efficiency and free up more time to focus on scientific thinking. |
Throughout this programme, you'll learn not only how these tools work, but when they should be used, their limitations and how to apply them responsibly within biological research.
Why choose this training?
Designed for biologists
The training has been developed specifically for researchers working in immunology, combining practical AI skills with real biological questions, datasets and research challenges.
While the principles you'll learn are applicable across the life sciences, every example and case study has been chosen to help immunologists relate the learning directly to their own research.
Learn from immunologists
Our trainers are computational immunologists with experience in both wet and dry lab research.
They know first hand what it's like to learn coding and computational biology for the first time and understand the challenges faced by researchers navigating an increasingly data rich research environment.
Rather than focusing on theory alone, they'll show you practical ways to integrate AI into your everyday research.
Practical from day one
Every course combines expert teaching with demonstrations and hands on exercises, giving you skills you can immediately apply to your own work.
You'll leave with practical workflows, resources and techniques to help you work more efficiently and confidently.
Meet the trainer
Dr Jason Cosgrove
Principal Scientist, Computational Medicine
Jason is a computational immunologist with more than 10 years experience across academia, biotechnology and the pharmaceutical industry. Specialising in computational biology, AI and systems immuno-hematology, he delivers practical, research focused training designed to help biologists confidently apply computational approaches in their research.
Who should attend?
This programme has been designed for researchers at different stages of their computational journey.
Whether you're completely new to AI or already working with computational methods, there's a course to match your level of experience.
Our courses are ideal for:
- PhD students and postdoctoral researchers
- Wet lab scientists
- Immunology researchers working in academia, industry or healthcare
- Clinicians involved in research
- Bioinformaticians
- Computational biologists
- Researchers looking to develop practical AI and machine learning skills
Each course clearly states any recommended programming experience or prior knowledge.
Our courses
Module 1: Building an AI research assistant
No programming experience required.
Learn how to turn today's leading AI tools into your own personalised research assistant.
Through practical demonstrations and guided exercises, you'll learn how to:
- Understand key AI concepts and large language models (LLMs)
- Build an AI research assistant tailored to your work
- Search, compare and synthesise the scientific literature
- Interpret omics datasets using AI tools
- Support grant writing, research planning and experimental design
- Critically evaluate AI generated outputs and use AI responsibly
Perfect for researchers who want to use AI more effectively in their work, whether or not they have coding experience.
Module 2: Incorporating AI into your analysis workflows
Basic R programming experience recommended.
Learn how to integrate AI directly into your computational workflows by calling AI models from R and building AI enabled pipelines to interpret omics datasets using trusted biological resources.
You'll learn how to:
- Understand when different AI approaches add value to omics workflows
- Choose between API, local and cloud based AI models
- Call AI models programmatically from R
- Build retrieval augmented generation (RAG) pipelines
- Apply AI to real immunology datasets and analysis workflows
Ideal for researchers working with omics data who want to incorporate AI into their existing analysis pipelines.
Coming soon
Module 3: Machine learning for immune profiling, prediction and biomarker discovery
Some prior R experience recommended.
Apply machine learning to profile immune cells, build predictive models and identify novel biomarkers using real immunology datasets.
You'll learn how to:
- Understand supervised and unsupervised machine learning
- Select the most appropriate algorithm for different biological questions
- Analyse scRNA-seq datasets using dimensionality reduction and clustering
- Build and evaluate machine learning models in R
- Apply machine learning to immune profiling and biomarker discovery
Ideal for immunologists and life scientists who want to apply machine learning techniques to their own data.
Coming soon
Course format
All courses are delivered live online through a combination of expert presentations, live demonstrations, guided practical exercises and interactive Q&A sessions.
Our focus is on practical learning, ensuring you leave with techniques, workflows and resources that you can apply immediately to your own research.
About C3Bioscience
C3Bioscience specialises in computational biology, bioinformatics and AI training for the life sciences. Their team combines expertise from academia, biotechnology and the pharmaceutical industry, delivering practical, research focused training that helps biologists apply AI with confidence.
