Teaching & Training
Around 530 hours of university teaching and many professional training sessions, turned into workshops. For research teams and doctoral schools, and for organisations that need to work with data and with AI tools sensibly.
Two audiences
Research
Doctoral schools, formation permanente at INRAE, CNRS and universities, research institutes, and workshop providers.
- Statistics and experimental design
- R and Python, from the basics to building an application
- Reproducible workflows and version control
- Genomics and data analysis
Organisations & companies
Teams that need to read their own data properly, and to use AI tools without either overtrusting or dismissing them.
- Data analysis foundations
- Working effectively with AI tools
- Using AI responsibly: limits, verification, governance
- Reading and questioning a quantitative result
Taught by someone who does the work
I teach the things I use: statistics and experimental design, R and Python, reproducible workflows, genomics and data analysis. The examples come from real projects — population genomics, ecology, forensic genetics — not from toy datasets.
Around 530 hours of university teaching, at the University of Bern (2015–2019: genomics and bioinformatics, statistics for biologists, introduction to R, population genetics seminars) and the University of Limoges (2012–2013). Alongside that, many professional training sessions delivered for a range of clients, in research and in industry. Sessions run in French or English.
What participants leave with:
- A design that will survive review, decided before the data is collected
- The ability to run and interpret their own analyses in R
- Workflows that another person — or their future self — can re-run
- A clear sense of what a given method can and cannot support
- Where AI tools genuinely help, and where they quietly do damage
Practical format
One to three days, on-site or remote, in French or English.
One day
A focused session on a single topic — an introduction to R, experimental design, or working with AI tools.
- • On-site or remote
- • French or English
- • Hands-on from the start
Two to three days
A full workshop with exercises on real datasets, and time for participants to work on their own data.
- • Exercises on real data
- • Participants’ own datasets welcome
- • Materials to keep
Tailored programme
Built with you around a specific team, project or curriculum — including sessions repeated across a doctoral school year.
- • Content designed with you
- • Repeatable sessions
- • Follow-up Q&A
Topics
Statistics & Experimental Design
Sampling, power, confounding, model choice. Getting the design right before the data exists.
R, Python & Web Apps
From data manipulation and visualisation in R and Python to building an interactive web application.
Reproducible Workflows
Version control, project structure, documented environments — analyses that can be re-run.
Genomics & Data Analysis
Population genomics and sequence data: what the methods assume and where they break.
Data Analysis Foundations
For non-specialists: reading a dataset, choosing a summary, and knowing when a result is thin.
Working with AI Tools
Practical use of LLMs and AI assistants, their failure modes, and responsible use in an organisation.
Funding
AlgoLife is not a Qualiopi-certified training provider, so French companies cannot have these sessions funded through their OPCO. Research institutions, doctoral schools and public bodies funding training directly are unaffected.
Planning a workshop?
Tell me who the participants are, what they already know, and how much time you have. I will propose a programme.