Pre-Doctoral Fellowship in Adaptive Multi-Modal Foundation Models
Join the Computer Vision Center to advance research in AI and machine learning.
Best for:Best for candidates with a strong foundation in computer vision and deep learning.
Overview
The Computer Vision Center (CVC) in Barcelona is offering a pre-doctoral fellowship for a candidate to engage in research on adaptive multi-modal foundation models. This position is linked to the project 'Adaptive Multi-Modal Foundation Models: Personalization, Efficiency, and Interpretability (AMPLE)'. The selected fellow will work full-time for a duration of four years, contributing to the development of personalized multi-modal agents and collaborating on various research tasks. Candidates are expected to have a strong background in relevant fields and a commitment to publishing their findings in top-tier conferences and journals.
What you'll do
- Conduct research on personalized multi-modal agents and their adaptation to new users or domains.
- Develop and apply continual learning theories within agentic AI.
- Collaborate with team members on various research tasks.
- Present and publish research findings in leading international conferences and journals.
- Submit annual work reports detailing objectives and plans.
Requirements
Degree required: MSc in Computer Vision, Artificial Intelligence, Computer Science, Computer Engineering, or a closely related field
Degree offered: PhD
Language: English; proficiency in written and spoken communication
Instruction: English
Publications: A track record of scientific publications is desirable
- Practical experience with deep learning techniques
- Strong written and spoken English communication skills
- Ability to work effectively in a team and independently
- Candidates must not have held a pre-doctoral contract for more than 12 months prior to application
- Must meet the accession requirements for a PhD program at Universitat Autònoma de Barcelona
Funding & terms
How to apply
Deadline: 2026-10-15
Candidates should apply by filling in the online application form. Required documents include a CV, academic record, and a cover letter, all submitted as a single file.
- CV
- Academic record
- Cover letter
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