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Fraunhofer-Gesellschaft

Student Assistant for applied Machine Learning in the »Quantum Computing« Team (all genders)

Fraunhofer-Gesellschaft Stuttgart, Baden-Württemberg, Germany

Research Services · 51-200 employees

Oct 21
machine-learning Junior (0-2 yrs) Part-time Germany
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About the role

You will develop and evaluate novel tensor network algorithms for machine learning and data analysis applications. Additionally, you will collaborate with researchers to advance the state of the art in quantum-inspired machine learning and contribute to scientific publications.

What they look for

Machine Learning Quantum Computing Tensor Networks Anomaly Detection Tensor Density Estimation Tensor Learning Algorithm Development Data Analysis Scientific Research Artificial Intelligence Programming Experimental Design

Requirements

The role is intended for a student interested in the intersection of quantum computing, machine learning, and artificial intelligence. Candidates should be motivated to contribute to interdisciplinary research and scientific outcomes.

Full description

The »Quantum Computing« team in the Digital Business research area at Fraunhofer Institute for Industrial Engineering IAO works on national and international research and consulting projects aimed at harnessing the potential of quantum computing for industry and society. Together with the Fraunhofer Institute for Manufacturing Engineering and Automation IPA, the team operates flaQship, the Fraunhofer Lab for applied quantum computing Stuttgart-Heilbronn. In this context, the team coordinates the »Kompetenzzentrum Quantencomputing Baden-Württemberg« Baden-Württemberg« and co-leads the office of the state initiative QuantumBW, and it offers a broad portfolio of training courses, symposia, trade fair appearances and knowledge transfer formats for companies, students and the general public.

Within this environment, our research group investigates tensor network methods for anomaly detection, combining concepts from quantum computing with state-of-the-art machine learning techniques. We develop novel tensor-based approaches, including tensor density estimation and tensor learning, to efficiently model and analyze high-dimensional data.

To support this research, we are looking for a motivated Student Assistant who would like to contribute to cutting-edge work at the intersection of quantum computing, machine learning, and artificial intelligence. As part of an interdisciplinary team, you will support the development and evaluation of innovative algorithms and have the opportunity to contribute to scientific publications and research outcomes.

Be part of change

  • You develop and evaluate novel tensor network algorithms for machine learning and data analysis applications
  • You investigate and implement advanced approaches such as tensor density estimation and tensor-based learning methods
  • You analyze research questions, design experiments, and assess the performance of newly developed methods
  • You work closely with our researchers to advance the state of the art in tensor network and quantum-inspired machine learning
  • You contribute to scientific publications and present research results to the international research community

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