Master Thesis in Foundation Model Compression Methods

Master Thesis in Foundation Model Compression Methods

• Full-time

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

The research team at the Bosch Center for Artificial Intelligence (BCAI) provides the opportunity for students in machine learning, computer vision, or a related field to complete their Master’s thesis with us. At BCAI, we build the foundation to generate real-world impact through cutting-edge research focusing on data/compute-efficient and generalizable AI. We design and implement AI for smart, connected, and autonomous technologies across Bosch business sectors.

• During your time at Bosch, you will get the chance to develop and implement novel algorithms, and evaluate them on multi-modal open and Bosch datasets.
• Together with BCAI researchers, you will perform original research, theoretical investigations, and aim to publish at top ML conferences.
• The goal is to define the topic that fits within the scope of a Master's thesis. Possible research areas include foundation model compression via knowledge distillation or dynamic compute to enable hardware-efficient neural networks.

Education: Master studies in the field of Computer Science, Mathematics, Physics or comparable with good grades.

Experience and Knowledge: in machine learning, deep learning, natural language understanding, robotics, or computer vision from previous internships, work, personal projects, and/or lab work; ideally good publication record with at least one publication at tier-1 conferences in the field of machine learning, robotics, natural language processing, or computer vision (e.g., NeurIPS, ICML, ACL, CVPR, ICLR, etc.) as well as excellent and proven programming skills, particularly in Python and PyTorch/TensorFlow.

Personality and Working Practice: a team-minded and analytical individual with a structured way of working.

Start: according to prior agreement
Duration: 6 months.

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin, or sexual identity.

Need further information about the job?
Lukas Schott (Functional Department)
+49 152 54529424