About this role
Summary: Join the Data-based Methods team at Fraunhofer ENAS to develop digital twins for semiconductor manufacturing using AI and machine learning. This role involves process optimization, model-based control, and applying semi-supervised approaches to handle limited reference data.
The particular strength of the Fraunhofer Institute for Electronic Nano Systems ENAS lies in the development of smart systems for various applications. The systems combine electronic components, micro and nano sensors, and actuators with interfaces for communication. Fraunhofer ENAS develops individual components, the technologies for their production, as well as system concepts and system integration technologies and transfers them into practical use. Fraunhofer ENAS accompanies customer projects from the idea to the design, technology development, or implementation using existing technologies right through to the tested prototype.
The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning, and computer vision. The main focus is on semiconductor manufacturing and medical technology.
Are you passionate about cutting-edge research at the intersection of semiconductor technology and artificial intelligence? Join us as a student in our team for artificial intelligence and data based methods at the Fraunhofer Institute of Electronic Nanosystems (ENAS).
We're on a mission to create digital twins for highly complex semiconductor manufacturing processes - enabling process optimization and model-based control. As the industry moves towards more individualized processes and products, a significant challenge is to enable accurate predictions with limited reference data. Therefore, we employ a broad range of artificial intelligence algorithms that enrich process data with sophisticated domain knowledge and semi-supervised approaches to incorporate unlabeled data.
We are currently looking for students for Master's theses, internships and student assistants. We can discuss our ideas together to find the right job for you.
What you contribute
- You already have some practical experience with AI and Machine Learning algorithms.
- Excellent programming skills (preferably with Python) are required.
- A strong interest in interdisciplinary work at the intersection of semiconductor technology, data science and artificial intelligence is essential.
- Still studying in Chemnitz, Mittweida or the surrounding area.
- An average grade < 2 is preffered.
We value and promote the diversity of our employees' skills and therefore welcome all applications – regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Our tasks are diverse and adaptable – for applicants with disabilities, we work together to find solutions that best promote their abilities.
With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.
Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.
Dr. Jan Langer will be happy to answer any questions you may have about the position. Please submit your questions here via our recruiting portal.
For data protection reasons, we can only consider applications submitted via our career portal - thank you for your understanding.
Fraunhofer Institute for Electronic Nano Systems ENAS
<a href="http://www.enas.fraunhofer.de<input id="tor\_\_fcustInstituteLinkRMK" type='hidden' value='27331' >" target="\_blank" rel="noopener">www.enas.fraunhofer.de
Requisition Number: 73236 Application Deadline:
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