STUDENT ASSISTANT – KNOWLEDGE-BASED ROBOT SYSTEM IN PRODUCTION (M/F/D)

Who are we?

fortiss is the research institute of the Free State of Bavaria for the development of software-intensive systems with headquarters in Munich. The scientists at the institute cooperate in research, development and transfer projects with universities and technology companies in Bavaria, Germany and Europe. The focus is on research on state-of-the-art methods, techniques, and tools for the development of software- and AI-based technologies for dependable and secure cyber-physical systems (CPS). fortiss is organized in the legal form of a non-profit limited liability company. Shareholders are the Free State of Bavaria (majority shareholder) and the Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.
To further strengthen our Platform Engineering team, we are looking for:
Student Assistant (m/f/d) – Knowledge-Based Robot System In Production
The Platform Engineering competence center of fortiss conducts applied research on knowledge representation and reasoning for cyber-physical production systems following the Industry 4.0 paradigm. We develop means to formally represent relevant information and data on products, services, manufacturing/value creation processes, and the hardware and software components involved therein. The goal is to increase the level of flexibility and autonomy in the design, development and operational phases of robot-based automation systems based on the integrated analysis and interpretation of the introduced semantic models.
We are looking for motivated students to join our team.

Your tasks:

• Literature review to explore state-of-the-art advancements in the fields of robotics, manufacturing, and Industrie 4.0, including comprehensive paper analysis to identify prevailing trends, methodologies, and technologies;
• Development of novel robotic programming and control methods on our industry-relevant demonstrators with various robots for more versatile and intelligent task execution;
• Formal knowledge modeling using Ontology (RDF/OWL/SPARQL) and integration of a knowledge database into the robot system;
• Deployment of an LLM model for generating context-specific knowledge within the knowledge base and the automatic task planning with automatic generation of SPARQL queries for extracting the contextual information

Your profile:

• Student in the fields of mechanical engineering, mechatronics, computer science, and electrical engineering;
• Strong background and interest in robotics, machine learning, and industrial automation;
• Previous project experience with proficiency in at least one of the programming languages (preferably C++, Python, Java);
• Proficient in programming under Linux (e.g. Ubuntu) and version control methods (e.g., Git);
• Experience in development under frameworks or standards in robotics or automation, such as Matlab, ROS/ROS2 (Robot Operating System), OPC UA, and MQTT;
• Familiar with deep learning frameworks such as PyTorch or Tensorflow. Experience with fine tuning of LLMs is a plus;
• Previous experience in knowledge graph with GraphDB or Protégé is a plus;
• Previous experience in HRI (Human-Robot-Interaction) design, such as UX (user experience) design or frontend design with Angular is a plus;
• Excellent communication and presentation skills in English or German.

Our offer:

• An international and dynamic work environment;
• Close cooperation and interaction with both academic and industrial professionals;
• Possibility to extend to Bachelor/Master thesis;
• Access to a diverse range of hardware, including robot arms (KUKA iiwa and UR5), industrial grippers, and sensors (Torch-force sensors, RGB-D cameras) for developing and validating your ideas;
• GPU array in lab for training deep learning models or LLMs;
• Possibilities for paper publication;
• Convenient location and view on 15 OG of Highlight Tower.

Did we catch your interest?

Please submit your application with a motivational statement, a detailed CV and a current transcript of records.
Job-ID: PEng-SH-02-2023
Contact: Junsheng Ding

Ralf Kohlenhuber

Human Resources Administrator