EMBL-EBI virtual course: Microscopy data analysis: machine learning and the BioImage Archive 2026
Apply by 7 December 2025.
Course dates: 16 – 20 March 2026
Fee: £240.00 academia / £340.00 industry (funding is available)
Contact: Omotoke Labiyi
Open application with selection: 40 places
Organisers
Virtual course
Participants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and asynchronous communication via Slack.
Pre-recorded material may be provided before the course starts that participants will need to watch, read or work through to gain the most out of the actual training event. In the week before the course, there will be a brief induction session. Computational practicals will run on EMBL-EBI's virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.
Participants will need to be available between the hours of 08:00 – 18:00 GMT each day of the course. Trainers will be available to assist, answer questions, and provide further explanations during these times.
Who is this course for?
This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content is also suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publicly available data curated in the BioImage Archive.
The course is accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python.
Recommended preparatory reading:
Learning outcomes
After the course, you should be able to:
During this course, you will learn about:
Data repositories
Analysis tools
Analysis tools are subject to minor changes.
Programme
All times in the programme are listed in GMT. No code has to be inserted here.
Apply by 7 December 2025.
Course dates: 16 – 20 March 2026
Fee: £240.00 academia / £340.00 industry (funding is available)
Contact: Omotoke Labiyi
Open application with selection: 40 places
Organisers
- Jean-Marie Burel, University of Dundee
- Patricia Carvajal Lopez, EMBL-EBI
- Matthew Hartley, EMBL-EBI
- Virginie Uhlmann, EMBL-EBI
- Teresa Zulueta Coarasa, EMBL-EBI
Virtual course
Participants will learn via a mix of pre-recorded lectures, live presentations, and trainer Q&A sessions. Practical experience will be developed through group activities and trainer-led computational exercises. Live sessions will be delivered using Zoom with additional support and asynchronous communication via Slack.
Pre-recorded material may be provided before the course starts that participants will need to watch, read or work through to gain the most out of the actual training event. In the week before the course, there will be a brief induction session. Computational practicals will run on EMBL-EBI's virtual training infrastructure, meaning participants will not require access to a powerful computer or install complex software on their own machines.
Participants will need to be available between the hours of 08:00 – 18:00 GMT each day of the course. Trainers will be available to assist, answer questions, and provide further explanations during these times.
Who is this course for?
This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content is also suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publicly available data curated in the BioImage Archive.
The course is accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python.
Recommended preparatory reading:
- BioImage Archive: A call for public archives for biological image data
- ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy
- EMPIAR: a public archive for raw electron microscopy image data
- Image Data Resource: a bioimage data integration and publication platform
- BioImage Model Zoo
- OME-Zarr: a cloud-optimised bioimaging file format with international community support
- Introduction to the Unix environment – https://swcarpentry.github.io/shell-novice/index.html
- Introduction to Python – http://swcarpentry.github.io/python-novice-gapminder/
Learning outcomes
After the course, you should be able to:
- Interact programmatically with the BioImage Archive and other data resources
- Interact with data formats for biological imaging data, particularly cloud-optimised formats
- Apply pre-built machine learning models to image data
- Train and retrain machine learning models on image data
- Utilise machine learning approaches for object detection, image segmentation, and de-noising
During this course, you will learn about:
Data repositories
Analysis tools
Analysis tools are subject to minor changes.
Programme
All times in the programme are listed in GMT. No code has to be inserted here.