The team you'll be part of is the world-renowned research arm of Nokia, having invented many of the foundational technologies that underpin information and communications networks and all digital devices and systems. This research has produced ten Nobel Prizes, five Turing Awards and numerous other awards.

Position: Bell Labs Internship - Autonomous Systems
Duration: 3-4 months
Location: Stuttgart, Germany
Start date: June, 2025
Education recommendation: Master student/Master student enrolled in PhD in Computer Science, Data Science, or related fields 
 

You have:

Good knowledge of ML concepts. A solid understanding of Python and its application in system-level programming and Deep Learning frameworks such as Pytorch. Familiarity with Federated Learning packages is an added plus. Have strong development skills (Python or RUST)  Strong problem-solving skills and the ability to approach complex challenges in a systematic and methodical manner.

It would be nice if you also had:

Knowledge about Kubernetes and containers Working knowledge of crypto networks Basic understanding how S3, NFS, SMB and WebDav work.
 
 

As part of our team, you will work on ONE or MULTIPLE of the following:

Design and implement research ideas in the fields of AI/ML, Large Language Models (LLMs), Intelligent Edge, etc.  Work on developing solutions using Deep Neural Networks, Reinforcement Learning, Federated Learning, etc.  Investigate the potential and challenges of Web 3.0 technologies in decentralized infrastructure. Explore and implement Self-Sovereign Identity (SSI) in different use-case scenarios.  Understand Smart Contracts / Crypto Networks and implement them for different use cases.  Implement an async Python package for the RUST-based Polkadot library. Investigate various options to proxy S3, NFS, SMB, and WebDAV connections. Install or implement such proxy methods for an AI/ML training platform. Understand, design, and implement GPU / Compute sharing solutions. Install and evaluate various Kubernetes gang schedulers with respect to GPU resources. Implement an example Kubernetes scheduler supporting heterogeneous hardware resources. Understand Smart Energy Management - dive into the nuances of energy vertical, work on consumption/presumption forecasting, and non-intrusive load monitoring for energy trading. Publish findings in reputable journals. Develops skills to carry out basic, repetitive, manual tasks that are described in detail and conducted under close supervision. Have no discretion to vary from established procedures.