Context-Aware AI Driver Assistant Internship

StudentJob; Helmond; On-site; EUR 6,000 - 7,000; Part time

Benefits offered by this job

€615 monthly internship allowance (MSc

Travel expense contribution

Jong TNO membership

Job summary

TNO invites motivated HBO or Master’s students to join the MARQ Digital Lab for an internship focused on building a context-aware AI driver assistant. You will design software architecture to connect a driving simulator (SCANeR) with a locally hosted LLM on MARQ Lab GPUs, study real-time context updates, and evaluate open-source LLM options for safe, timely driver support.

The project aims to deliver a working proof-of-concept, integrating with multiple simulation platforms and providing

Qualifications

• Able to integrate with driving simulator software using C++ and Python.
• Knowledge of AI/ML, LLMs, and prompt engineering for real-time context handling.
• Experience with REST APIs, Linux basics, Docker, and Git.
• Familiarity with ROS 2, Kubernetes, or GPU computing is an advantage.
• Interest in automated driving (ADAS/CCAM) and Human-AI Interaction.

Responsibilities

• Develop a context-aware AI driver assistant for driving simulations.
• Architect system integration between SCANeR and a locally hosted LLM on MARQ Lab GPUs.
• Explore data/context update frequencies for real-time interaction.
• Evaluate open-source LLM suitability for context-aware driver assistance.
• Produce a proof-of-concept and perform system performance evaluations.

Skills

C++

Python

AI/ML knowledge

LLMs & prompt engineering

REST APIs

Linux

Docker

Git

ROS 2

Kubernetes

GPU computing

Interest in ADAS/CCAM

Education

Enrollment in HBO or Master’s program

Tools

Docker

Git

ROS 2

Kubernetes

GPU computing

Job description

TNO invites motivated HBO or Master’s students to join the MARQ Digital Lab for an internship focused on building a context-aware AI driver assistant. You will design software architecture to connect a driving simulator (SCANeR) with a locally hosted LLM on MARQ Lab GPUs, study real-time context updates, and evaluate open-source LLM options for safe, timely driver support.

The project aims to deliver a working proof-of-concept, integrating with multiple simulation platforms and providing