Research assistant
vor 4 Wochen
Research assistant (m/f/d) in the field of Safety Architectures for Machine-Learning-Based Systems
24.05., Wissenschaftliches Personal
The Chair of Engineering Resilient Cognitive Systems of the School of Computation, Information and Technology of the Technical University of Munich in Garching, is looking for a Research assistant (m/f/d) in the field of Safety Architectures for Machine-Learning-Based Systems to start as soon as possible.
About us
Cognitive cyber-physical systems are the key innovation drivers in many industries, such as autonomous vehicles, robotics, intelligent manufacturing systems, or medical devices. In many areas, traditional software systems are reaching their limits to further increase productivity and automation. That's why artificial intelligence is playing an important role. However, cognitive cyber-physical systems are more than AI; they are complex, software-intensive systems that use AI. Moreover, cyber-physical systems (as opposed to traditional interactive systems such as smartphones or web apps) interact with the real world. And while a failing smartphone app is annoying, a failing autonomous driving system can easily become life-threatening. Therefore, the development of high-quality systems, above all, safe and reliable systems, is central to the success of cognitive cyber-physical systems. We call those resilient cognitive systems.
The chair is still young and offers you the chance to shape something new. At the same time, it is closely connected to the Fraunhofer Institute for Cognitive Systems IKS so that the resources, competencies, and infrastructure of a Fraunhofer Institute are given to shape something great.
Tasks
Join our groundbreaking project in collaboration with the prestigious Fraunhofer Institute for Cognitive Systems IKS and be at the forefront of revolutionizing safety in artificial intelligence and machine learning applications. This is a unique opportunity to collaborate with world-class experts in a dynamic team dedicated to tackling one of the most critical challenges of our time: Ensuring the safety of cyber-physical systems powered by machine learning.
In an era where machine learning's capabilities are expanding at a breathtaking pace, its application in fields such as autonomous vehicles, robotic assistants, and life-saving medical devices is both awe-inspiring and cautionary. The potential is limitless, yet the margin for error is virtually non-existent. Despite its impressive achievements, machine learning can unpredictably fail, highlighting the urgent need for robust safety mechanisms that protect users from harm.
This is where you come in.
We are seeking visionary researchers and engineers passionate about pioneering the development of new safety architectures for machine learning-based systems. Your mission will be critical: To ensure that these systems can be trusted with people's lives. You will dive deep into the exploration of innovative safety patterns, evaluating their effectiveness and constraints, and ultimately crafting a concrete solution that guarantees the safety of machine learning-based object detection systems in robotic applications.
By joining our team, you will not only contribute to making advanced machine learning applications safe for widespread use, but you will also be part of setting a global standard for safety in technology. This project offers the chance to work on cutting-edge research, publish influential papers, and make a real-world impact that could save lives.
If you are driven by the challenge of solving complex problems, eager to make a tangible difference, and ready to shape the future of safe intelligence, we invite you to apply. Together, we can build safe systems that harness the power of machine learning to improve lives and society.
What we require
You can become part of our team in this exciting research field if you have the following qualifications:
Completed university studies (Master) in the field of computer science or in a computer science-related course of study. Quick comprehension, creativity, and independent working Ability to work in a team to jointly develop solution Very good English skills - written and spokenKnowledge of safety engineering and machine-learning would also be beneficial (but not required).
What we offer
Remuneration in accordance with pay group 13 of the Bavarian TV-L, subject to personal requirements. The position is initially limited to two years with an option to extend for a further two years. The opportunity to pursue a doctorate in the graduate school at one of Europe's leading universities of excellence with consistently high rankings High level of personal responsibility and creative freedom Access to an excellent international network in science and industry Access to the resources and infrastructure of one of the leading Fraunhofer institutes.Application
Please send your complete application documents (including CV, letter of motivation and transcript of records) by e-mail to . We are also available by e-mail at this address for any queries.
Severely disabled persons are given preference in the event of equal suitability and qualifications. TUM promotes equality between women and men, and applications from women are therefore expressly welcomed.
Technical University of Munich
School of Computation, Information and Technology
Chair of Engineering Resilient Cognitive Systems
Prof. Dr. Mario Trapp
Arcisstrasse 21, Munich
Note on data protection: When applying for a position at the Technical University of Munich (TUM), you submit personal data. Please refer to our data protection information in accordance with Art. 13 of the General Data Protection Regulation (DSGVO) regarding the collection and processing of personal data as part of your application. By submitting your application, you confirm that you have taken note of the TUM data protection information.
Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. Schwerbehinderte Bewerberinnen und Bewerber werden bei ansonsten im wesentlichen gleicher Eignung, Befähigung und fachlicher Leistung bevorzugt eingestellt.
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