Scientist : Machine Learning for Sensor-Systems
Role details
Job location
Tech stack
Job description
As part of the interdisciplinary "Machine Learning Enhanced Sensor Systems" team, you will work on and develop data-driven solutions for intelligent sensor technology and sensor networks. This includes, in particular:
- Developing and implementing machine learning and deep learning models for evaluating sensor data
- (Pre-)processing and analyzing time- and frequency-based sensor data (signal processing, feature engineering)
- Setting up and maintaining data pipelines: sensor data acquisition, storage (e.g., databases), and processing
- Use of Python (including TensorFlow, PyTorch) for ML applications, training, evaluation, and deployment of models
- Use of GPU-based servers and modern IT infrastructure for training and inference
- Application of classical ML methods (e.g., regression, classification) and neural networks (e.g., CNNs, RNNs)
- Preparation of ML models for inference on edge systems (edge AI, embedded AI)
- Collaboration on MLOps/DevOps processes (e.g., versioning, automation, CI/CD)
- Collaboration in national and international research projects
- Publication of results in journals and at conferences
- Creation of structured technical documentation of development results
What you contribute
You are familiar with research and development work in a university environment and enjoy familiarizing yourself with new topics. Ideally, you meet the following requirements, * The opportunity to pursue a doctorate
- Collaboration in an interdisciplinary and dedicated research team
- Access to modern computing resources and research infrastructure
- Flexible working hours and opportunities for personal development
- Remuneration in accordance with TVöD EG13 (depending on qualifications).
- A fixed-term position for 3 years.
In addition, we offer you a unique mix of dynamism, flexibility, and teamwork. Due to flat hierarchies and rapid change, we need employees who think ahead and come up with straightforward solutions. This is crucial to our success. That is why we support people who are committed to our company and offer them opportunities for on-the-job development. You can expect an open-minded and dynamic team that will place its trust in you from the outset and enable you to work independently.
The weekly working time is 39 hours. This position is also available on a part-time basis. 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. Appointment, remuneration and social security benefits based on the public-sector collective wage agreement (TVöD). Additionally Fraunhofer may grant performance-based variable remuneration components.
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.
Requirements
- Successfully completed scientific university degree, preferably in computer science, electrical engineering, or a comparable field of study
- Very good programming skills in Python (e.g., TensorFlow, PyTorch)
- In-depth knowledge of machine learning/deep learning (statistics and data analysis)
- Good knowledge of sensor technology and sensor applications
- Experience in handling sensor data and sensor networks, as well as their collection, storage, and processing
- Ideally, initial experience with DevOps/MLOps approaches
- Techniques for reducing ML model size and complexity in order to run them on limited hardware
- Strong analytical and solution-oriented thinking
- Independent, structured, and self-reliant way of working
- Very good communication skills for collaborating with industry partners and project teams
