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Student Assistant – Quantum Machine Learning / Quantum Circuit Compilation
The »Self-Learning Systems« group at Fraunhofer IIS is part of the »Precise Positioning and Analytics« department and located at the institute’s Nuremberg site. Our team members have diverse academic backgrounds from computer science, engineering and physics. One of our research goals is to provide robust and safe algorithmic solutions for control and decision-making tasks in complex dynamic environments. To this end, we employ a range of approaches from machine learning and deep learning in combination with reinforcement-learning techniques. This allows us to generate adaptive decision-making policies capable of processing high-dimensional state representations, such as camera images and other types of sensor readings.
»Quantum Machine Learning« is an emerging research field at the cross section of artificial intelligence and quantum computation. Spurred by the recent experimental demonstration of so-called quantum advantage, the research community has increased its efforts to propose new types of quantum algorithms for the still imperfect quantum computing devices we have today. In the »Self-Learning Systems« group, we want to explore the overlap of quantum computing and machine learning. On the one hand, adaptive quantum algorithms (so-called variational quantum circuits), can be integrated into our machine learning and reinforcement-learning based solutions. On the other hand, machine learning can assist the implementation of algorithms. »Quantum circuit compilation« is a challenging task given the NISQ (noisy intermediate scale quantum) era quantum computers of today.
[b]Would you like to expand your programming skills and learn about interesting new topics in the field of quantum machine learning?
Then have a look at our offer!
What you will do
• You work on topics related to quantum machine learning and quantum circuit simulation
• You build up an understanding of physical error models for multi-qubit systems
• You work with quantum computing frameworks (Qiskit, Cirq, PennyLane, Tensorflow Quantum, …)
• You use existing and emerging quantum hardware in the Munich Quantum Valley
What you bring to the table
• You are currently enrolled in a physics, mathematics or computer science program
• You are familiar with python programming and libraries like NumPy, SciPy etc.
• You speak English fluently
• You have already worked with frameworks like Tensorflow (Keras) or PyTorch
• You have some machine learning and deep learning background
What you can expect
• Flexible working hours
• Open and friendly team work
• Varied tasks with room for creativity
• Exciting seminars and events
• Networking with scientists
• Active contribution in applied research
• Mentoring program »josephine©« for talented female students
Weekly working hours are determined by agreement, minimum are 10 hours a week. You can reduce your hours before exams and increase them during semester breaks. You can flexibly determine the working days. After your studies, you have the option of working with us full or part time.
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.
Apply online now (PDF: cover letter, CV, transcripts): https://jobs.fraunhofer.de/job/Nürnberg-Student-Assistant-Quantum-Machine-Learning-Quantum-Circuit-Compilation-90411/775883301/ We look forward to getting to know you!
Fraunhofer-Institute for Integrated Circuits IIS
Requisition Number: 17942 Application Deadline: none Location: Nürnberg