The fully financed worldwide EEML Summer School 2026, which will be held in Cetinje, Montenegro, from July 27 to August 1, 2026, is currently accepting applications. The Eastern European Machine Learning Summer School is intended to give researchers and students from all around the world top-notch instruction in machine learning (ML) and artificial intelligence (AI). The program seeks to foster international academic interchange while advancing AI research and cooperation throughout Eastern Europe.
For an intense week of lectures, tutorials, and interactive sessions, the summer school brings together top scholars, professionals from the industry, and gifted students. Deep learning, natural language processing, computer vision, reinforcement learning, probabilistic models, and responsible AI are just a few of the advanced machine learning subjects that participants will learn about. Attendees will gain a deeper understanding of AI research and practical applications by combining theoretical underpinnings with practical insights.
High school kids, undergraduate and graduate students, PhD candidates, and postdoctoral researchers with a keen interest in AI and machine learning are all eligible to apply. Applications are accepted from all nations, making this a very competitive and diverse global opportunity.
Subject to funding availability, selected participants will receive full financial support, which includes registration fees, lodging for the duration of the program, and travel expenses. This guarantees that gifted people from all socioeconomic backgrounds can take part. Participants will gain from networking opportunities, peer collaboration, and face-to-face engagement with subject matter experts in addition to academic learning. Participants usually obtain a certificate of participation upon completion, which can improve their academic and professional profiles.
An online form and accompanying materials, including a resume, academic transcripts, a motivational statement, and occasionally a recommendation letter, are typically required for the application process. Academic excellence, a proven interest in AI and ML, and the caliber of the application materials are the criteria used in the competitive selection process.
Host Country: Cetinje, Montenegro
Funded By: The Eastern European Machine Learning Summer School
Opportunity Type: Summer Course
Date: 27 July to 1 August 2026
Eligibility Country: International Students
Financial Coverage: Fully Funded,
Eligibility Criteria:
- Open to All: Regardless of where they live, anyone who is 18 years of age or older may apply.
- Diverse Academic Backgrounds: Participants are encouraged to come from a variety of fields and disciplines.
- Global Participation: Applications from all around the world are invited, even though the program is held in Eastern Europe to highlight local machine learning competence.
- Inclusive Community: The program’s goal is to create a vibrant, globally diversified community of practitioners and researchers.
Benefits:
- Travel Support: Depending on funding circumstances, financial aid to pay for round-trip travel costs to and from the program site.
- Accommodations: For the duration of the summer course, free housing is offered.
- Coverage of Registration Expenses: For a limited number of participants, program registration expenses are fully waived.
- Certificate of Participation: An official document given to participants upon successful program completion.
What Are You Going to Get?
- Gain a thorough understanding of both basic and complex machine learning concepts, such as reinforcement learning and deep learning.
- Learn the fundamental terms, tenets, and ideas that are employed in the discipline.
- Examine the theoretical underpinnings, ongoing research issues, and new developments in artificial intelligence.
- Learn about popular neural network architectures, including Transformers, CNNs, RNNs, and GNNs.
- Recognize effective practices for designing experiments, setting baselines, and fine-tuning hyperparameters.
- Gain hands-on experience designing, training, testing, and assessing neural networks for practical uses.
- Discover how to assess model performance and identify typical problems such as overfitting.
- Through debates, poster sessions, and networking events, make connections with other attendees and top scholars.
Deadline: 31st March 2026
