Please read the call for applications carefully before starting your application to confirm that you meet the eligibility requirements for EASY-ML 2027.
Background
Applications are invited from MSc and PhD students and early-career scientists from universities and research institutions across East Africa to participate in the East African School for Young Researchers on Advanced Machine Learning Techniques (EASY-ML) 2027.
EASY-ML 2027 is a two-week, fully residential training programme designed to introduce participants to the fundamental concepts, algorithms, techniques, and practical applications of Machine Learning (ML), with particular emphasis on scientific research and data-driven problem solving.
The programme will combine theoretical lectures, hands-on practical sessions, applied case studies, and participant-led projects. Participants will gain practical experience in using machine learning tools and frameworks to analyse data, develop and evaluate models, and address complex problems within their respective fields. The training also seeks to promote innovative research approaches, effective problem solving, professional networking, and interdisciplinary collaboration.
The training aims to enable participants to:
• Understand the fundamental principles of machine learning;
• Apply suitable machine learning algorithms to scientific and research problems;
• Analyse and interpret complex datasets using modern computational tools;
• Develop and evaluate machine learning models.
Training Content
The training programme will cover:
• Introduction to Machine Learning (ML)
• Ethics in Artificial Intelligence and Machine Learning
• Regression and Classification
• Artificial Neural Networks
• Unsupervised Learning
• Advanced ML techniques (CNNs, BDTs, GNNs, Transformers, etc.)
• Hands-on Practice with Machine Learning Frameworks
• Applied Machine Learning Case Studies
• Participant-led Projects
Eligibility
Applications are encouraged from the following groups in East African universities and research institutions:
• MSc students
• PhD students
Applicants should:
• Be willing to participate actively in practical exercises and projects;
• Be available to remain fully resident for the entire duration of the training;
• Basic knowledge of Python programming is an added advantage but not mandatory;
• Participants in EASY-ML 2025 are not eligible.
Key Information
|
Item |
Details |
|
Training Dates |
13–27 January 2027 |
|
Venue |
Chiromo Campus, University of Nairobi, Kenya |
|
Mode |
Physical; fully residential |
|
Application Deadline |
30 October 2026 |
|
Training Fee |
Covered |
|
Travel Support |
Bus fare will be reimbursed. |
Application Instructions
1. Open the application page
Click the Apply link on the event page. You will see two separate forms:
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Application to the East-African School for Young Researchers on Advanced Machine Learning Techniques for applicants.
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Supervisor / Academic Referee Recommendation Form for supervisors or academic referees.
If you are applying to participate in the school, select the Application Form for Applicants.
2. Complete the applicant form
Complete all required sections of the application form carefully. These include:
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Personal and contact information
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Academic and research background
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Programming and machine-learning experience
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Motivation and expected benefit from the school
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Proposed participant-led project
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Participation commitment
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Supervisor / academic referee information
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Applicant declaration
You will also be required to upload supporting documents, including:
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Certificate of your highest qualification
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Latest academic transcripts, if you are currently a student
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A signed declaration page containing your full name, signature, and date
Please ensure that all information provided is accurate and complete before submitting your application.
3. Identify a supervisor or academic referee
As part of the application, you must provide the details of a suitable referee.
Your referee may be:
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Your research supervisor
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Your academic supervisor
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Your Head of Department
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Another academic or research professional who knows your academic or research work well
You will be asked to provide their:
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Full name
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Title/designation
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Department or unit
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Institution
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Institutional email address
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Telephone number
The referee must use an official institutional email address. Personal email addresses such as Gmail, Yahoo, or Outlook should not be used.
4. Send the referee form to your supervisor/referee
After identifying your referee, send them the Supervisor / Academic Referee Recommendation Form:
https://indico.in2p3.fr/event/40435/registrations/5282/
You must confirm in your application that you have shared this link with your referee.
It is the applicant's responsibility to contact the referee early enough and follow up to ensure that the recommendation is submitted before the deadline.
5. What the referee needs to do
The referee should open the Supervisor / Academic Referee Recommendation Form and complete the requested information about:
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The applicant
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Their own academic/professional details
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Their relationship to the applicant
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How long they have known the applicant
The referee should then prepare a confidential recommendation letter commenting on:
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The applicant's academic and/or research ability
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The applicant's suitability for EASY-ML 2027
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The applicant's potential to apply machine learning in their work
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How long and in what capacity they have known the applicant
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Whether they recommend the applicant for the programme
If the referee is recommending more than one applicant to EASY-ML 2027, they should rank the applicants in order of preference.
6. Submission of the recommendation letter
The recommendation letter should:
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Be written on official institutional letterhead
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Include the referee's name and position
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Include the institution and official contact details
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Be signed and dated
The referee must send the recommendation letter directly to:
After sending the letter, the referee should complete the confirmation in the recommendation form indicating that the recommendation letter has been submitted.
7. Check your application before submission
Before submitting your application, confirm that:
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All required sections have been completed
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All required supporting documents have been uploaded
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The referee's correct institutional contact details have been provided
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The referee form link has been shared with your referee
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You have confirmed your availability for the full programme period
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You have completed the applicant declaration
Applications with missing required information or supporting documents may not be considered.
8. Important dates
Application deadline: 30 October 2026, 11:59 PM
Both the applicant's application and the referee's recommendation must be completed by the deadline.
Recommendation letters received after 30 October 2026 will not be considered.
Please prepare your application carefully and answer all questions in your own words.
The use of AI in any section of the application form may result in the application being rejected.