Master Thesis Projects in Multimodal Foundation Models
12.11.2024, Nichtwissenschaftliches Personal
Are you fascinated by the possibilities of ChatGPT and the latest AI technologies? Imagine harnessing the immense power of Nvidia A100/H100 GPUs, each with 80GB of memory, to push the boundaries of AI innovation.
If you're passionate about multimodal foundation models and eager to explore the integration of vision, language, and other modalities in a unified framework, we invite you to join our cutting-edge research team. Dive into the future of AI by developing models that can understand and interact across multiple forms of data, unlocking new levels of intelligence and versatility.
We are seeking talented students to mentor, aiming to produce high-quality publications with practical applications. Our team has a strong track record, with publications in top international conferences such as NeurIPS, ICLR, ACL, EMNLP, NAACL, AAAI, UAI, and CIKM. Equipped with state-of-the-art Nvidia A100 GPUs, our research is at the forefront of multimodal machine learning, capable of handling extensive datasets and complex algorithms.
As part of this thesis project, you will have the opportunity to collaborate with a diverse group of PhDs, postdoctoral researchers, and professors from prestigious institutions like TUM, LMU, and others worldwide. This experience will allow you to develop advanced research skills and delve into the exciting field of World Models. Join us on this groundbreaking research journey and make a significant impact in the world of AI.
Topic Description:
Concrete topics in Multimodal Foundation Models
Requirements:
· Study in computer science, electrical engineering, physics, or mathematics;
· Good understanding of machine learning and deep learning;
· Good understanding of transformer as a plus;
· Good programming skills in Python and at least one deep learning framework;
· Independent working
Additional Information:
Start: from now on
Length of the work: 6-9 months
The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.
Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.
Kontakt: cognitive.yunpu@gmail.com, xingcheng.zhou@tum.de