Master Thesis: Multimodal Demo Selection

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About this opportunity

This master's thesis will explore Multimodal Feedback-Guided Demonstration Selection. The goal is to move beyond simple similarity-based retrieval and investigate whether demonstrations can be selected based on their actual usefulness to the model. The project will consider the complete demonstration - image, question/instruction, and answer; and will investigate how multiple complementary demonstrations can be selected efficiently.

The topic sits at the intersection of multimodal AI, retrieval, LLMs, in-context learning, and representation learning, with opportunities for both practical system development and research-oriented experimentation.

What you will do

You will design, implement, and evaluate a demonstration-selection framework for Large Multimodal Models.

Your work will include:

• Reviewing research on multimodal in-context learning, demonstration retrieval, vision-language models, and feedback-guided prompting
• Implementing baseline retrieval methods such as random, visual similarity, textual, and multimodal retrieval
• Building a feedback-guided retriever that learns which demonstrations are useful based on changes in LMM performance
• Representing demonstrations using their image, question/instruction, and answer, rather than visual information alone
• Investigating a selection of multiple demonstrations while considering usefulness, diversity, and redundancy
• Evaluating the impact of demonstration ordering and context size
• Conducting experiments on established multimodal benchmarks using open-source LMMs
• Performing ablation studies to understand which components contribute most to performanc
• Comparing the proposed approach with existing methods, including GRIP
• Analyzing results and documenting findings in a master's thesis and, where appropriate, a research publication

The skills you bring

• Background in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, or a related field
• Good programming skills in Python
• Knowledge of machine learning and deep learning
• Familiarity with PyTorch or similar ML frameworks
• Basic understanding of Transformers and neural representation learning
• Interest in Large Language Models, Computer Vision, NLP, or Multimodal AI
• Experience with Hugging Face, vision-language models, GPU computing, or contrastive learning is an advantage
• An analytical and research-oriented mindset, with the ability to design experiments and interpret results independently

Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

Primary country and city: Sweden (SE) || Stockholm

Req ID: 790806