Working Student - Energy-Efficient AI for Embedded Vision Systems (all genders)

• Model Development: You train and adapt spiking neural network (SNN) models using PyTorch-based SNN frameworks such as SpikingJelly or snnTorch for image classification on public benchmark datasets such as CIFAR-10.
• Hardware Deployment: You deploy trained models to neuromorphic edge AI hardware and work with hardware-specific constraints.
• Live Demo: You build a simple live demo using a camera for real-time image classification on the hardware.
• Power Measurement: You measure power and energy consumption and compare the results with existing solutions.
• Performance Evaluation: You compare different platforms based on accuracy, latency, and energy consumption.
• Documentation: You document your setup, results, and key findings., * Organize your schedule: Benefit from flexible working hours that are perfectly compatible with your studies.
• Become part of a creative team: Experience an open and friendly working atmosphere in which your ideas are valued.
• Variety that inspires: Look forward to divers tasks that inspire and challenge you.
• Shape the future with us: Take part in application-oriented research and put your theoretical knowledge to practice.
• Innovation that inspires: Exciting and pioneering projects that make a real difference.

We will agree your start date and weekly working hours with you individually (as a working student 10 to 20 hours per week). You can reduce your hours before exams and increase them during semester breaks. You can set your working days flexibly. After your studies, there are attractive opportunities to join the institute on a full-time or part-time basis. You can flexibly determine the working days of your fixed-term employment contract.

We would be happy to offer you the opportunity to write a bachelor’s or master’s thesis in cooperation with us in the above-mentioned subject area. The thesis will be assigned and carried out in accordance with the rules of your university. For this reason, please discuss the thesis with a professor who can advise you over the course of the project.

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 people are given preference if they are equally qualified. About Fraunhofer

• You are currently studying electrical engineering, computer science or a related field.
• You have experience with Python and are comfortable with PyTorch (model definition, training loops, saving/loading weights).
• You have basic CNN/ computer vision knowledge (conv layers, pooling, image classification pipelines).
• You bring a debugging mindset for hardware-in-the-loop work.
• First experiences with embedded systems, C or microcontroller skills are a plus.
• Basic understanding of power/energy metrics are an advantage.

Pulled from the full job description

• Flexible schedule

Our »AI-Based Communication Systems« group is part of the »Communication Systems« division at Erlangen. Our core focus is wireless communication research, but a subset of our projects extends into embedded AI and low-power edge vision, exploring how algorithmic solutions including neuromorphic and spiking neural network approaches can be deployed efficiently on embedded hardware. This student job sits within that latter track, working on spiking neural network models for vision tasks and their deployment on low-power neuromorphic hardware., The Fraunhofer-Gesellschaft is one of the world’s leading applied research organizations. 75 institutes develop cutting-edge technologies for our economy and society - more precisely: 32 000 people working in technology, science, administration and IT. They know: If you join Fraunhofer, then you are able to promote change. For themselves, for us and for the markets of today and tomorrow.