Training
AI for biomedical research
We train researchers and R&D teams to use AI reliably in biomedical and biological research. Our trainers are bioinformaticians who use these tools every day, on real omics projects.
Seminar · one-day course · 2-day omics workshop · in-house training for biotechs
Why it matters
AI is already in your lab. The question is how to use it without putting your results at risk
Large language models write, summarise, code and search the literature in seconds. They also invent references, produce code that runs without error yet returns a wrong result, and can expose data that should never have left the lab. We give research teams the understanding and the verification habits they need to benefit from AI while keeping full control of their science.
Who it is for
Researchers and R&D teams in biology and medicine
Designed for scientists who use, or are about to use, AI in their daily work.
Academic research
For researchers, engineers and PhD students in biology and medicine.
- Literature search and synthesis you can trust
- Analysis scripts you understand and can check
- Publisher and funder rules on AI use
- Reproducibility and transparent reporting in your methods
Biotech R&D teams
For R&D, data and regulatory teams in biotech companies.
- Confidentiality, trade secrets and patent risk
- Clear rules on which data may be submitted to which tool
- Productivity gains without compromising compliance
- An AI usage charter tailored to your company
No programming prerequisites for the core training.
Learning objectives
What participants are able to do after the training
Understand LLMs
Explain how a large language model works, and why it makes the errors it makes.
Choose the right tool
Pick the type of AI tool that suits a given research task, and know its limits.
Protect your data
Know which data can and cannot be submitted to an AI: health data, confidentiality, intellectual property.
Verify the output
Check an AI output (a reference, a piece of code, a result) before relying on it.
Set team rules
Draft the AI usage rules for your team: permitted tools, prohibited data, mandatory checks, disclosure.
Topics covered
From first principles to concrete omics use cases
We put the effort on what lasts: principles, risks and methods. Tools are presented as dated examples.
AI fundamentals
- AI, machine learning, deep learning, generative AI
- Tokens, pre-training and instruction tuning
- Context window, hallucinations, variability of answers
- Reasoning models, web search, code execution, agents
A brief history
- From the first artificial neurons to the Transformer
- Waves of enthusiasm and disillusionment
- The protein structure prediction breakthrough in biology
Tool landscape by category
- General-purpose assistants
- Literature search and synthesis tools
- Coding assistants and agents
- Specialised biological models
- Open models that run locally
Risks and good practice
- Health data and personal data under the GDPR
- Confidentiality and intellectual property
- Scientific integrity: invented references, plagiarism, disclosure
- Bias, overconfidence and silent errors in code
- Reproducibility and the European AI Act
Hybrid approaches
- The core analysis stays in a validated, versioned pipeline
- AI helps write, document and interpret it
- Human validation at every high-stakes step
- Local models for sensitive data
Use cases in omics
- Cell type annotation in single-cell data
- Interpreting a gene list
- Drafting the methods section
- Literature monitoring
Hybrid approaches
AI where errors are easy to catch, validated methods where they are costly
Good practice is neither "all AI" nor "no AI". We show how we combine both in real projects.
Single-cell cluster annotation
AI: proposes cell types from marker genes.
Human check: consistency with the biology and the literature.
Interpreting a gene list
AI: summarises known functions and suggests pathways.
Human check: verification in reference databases (GO, Reactome).
Drafting the methods section
AI: produces a first draft from the code and parameters.
Human check: exact versions and parameters.
Literature monitoring
AI: sorts and summarises new publications.
Human check: reading the articles that matter.
Biotech dossiers and protocols
AI: structures and rephrases.
Human check: regulatory compliance and confidentiality.
Formats
Four formats, from a first introduction to a tailored programme
Seminar
A solid introduction for a laboratory, department or seminar series.
- How large language models work, and why they fail
- The main risks for research data and integrity
- Simple verification habits
- Open to the whole team, no prerequisites
One-day course
Our core training: using AI reliably in biomedical research.
- Fundamentals, tool landscape, risks and compliance
- Hands-on literature and code workshops
- Hybrid approaches and omics use cases
- A draft AI charter for your team
Hands-on omics workshop
For bioinformaticians and biologists who code a little.
- Day 1: coding assistants applied to bulk and single-cell RNA-seq on public data
- Writing, reviewing and integrating scripts into an existing pipeline
- Day 2: assisted biological interpretation, methods and figures
- Prerequisites: the one-day course or equivalent, basic R or Python
In-house training for biotechs
A programme adapted to your company, on dates agreed with your team.
- Preliminary scoping interview
- Use cases drawn from your R&D, regulatory or clinical teams
- Emphasis on confidentiality, trade secrets and intellectual property
- An AI usage charter tailored to your company
Why train with us
Taught by bioinformaticians who use AI every day
Practising bioinformaticians
Our trainers apply AI to real omics projects. Examples come from that practice, not from a generic AI course.
Real biological data
Workshops run on real public biological datasets, so participants work on problems that look like their own.
Errors made visible
Every workshop exposes participants to AI errors and teaches them how to catch them. That is what they remember.
Updated at every session
Tools and rules change fast. Demonstrations, tool landscape and regulatory content are reviewed before each session.
Upcoming sessions
Be the first to know
Registration
- Registration for upcoming sessions is not open yet. Leave your details through our contact form and we will notify you as a priority as soon as it opens.
- Hands-on workshops are run in small groups, with a maximum of 12 participants.
- In-house training is organised on dates agreed with each team.
Frequently asked questions
Do I need programming skills?
No. The seminar and the one-day course require no programming background: they are designed for researchers, engineers and PhD students in biology and medicine. The two-day hands-on omics workshop is aimed at bioinformaticians and biologists who already code a little, and assumes the one-day course or an equivalent level, plus basic R or Python.
Which format should we choose?
The seminar suits a laboratory, department or seminar series that wants a solid introduction to how AI works and where it fails. The one-day course is our core training, with hands-on workshops and a draft set of AI usage rules for your team. The two-day workshop goes further on omics analysis. Biotech companies usually opt for in-house training tailored to their teams. We are happy to help you choose.
Do you recommend specific AI tools?
We present tools by category of task rather than as a list of products, because the categories last while the products change every few months. The tool landscape is reviewed and updated before every session, and we focus on the criteria that matter for research: where data is processed, confidentiality, reliability and traceability.
What data do participants work on?
Workshops use public or simulated biological datasets, never client data. The examples come from real omics projects, anonymised. For in-house training, the use cases are prepared with your teams during a preliminary scoping interview.
Can you train our team in-house?
Yes. In-house training is organised on dates agreed with each team. It follows the structure of the one-day course, adapted beforehand to your use cases (R&D, regulatory, clinical), with particular attention to confidentiality, trade secrets and intellectual property. It ends with an AI usage charter tailored to your company.
When are the next sessions?
Registration for upcoming sessions is not open yet. Leave your details through our contact form and we will notify you as a priority as soon as it opens.
Interested in this training?
Tell us about your team and your needs. We will notify you as a priority when registration opens, or get back to you to organise an in-house session.
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