The proliferation of Internet-connected and sensor-enabled technologies, including autonomous vehicles, wearables, smartphones, and other IoT devices, has resulted in distributed systems that generate vast quantities of data with a high velocity and volume. This abundance of data presents compelling opportunities for deep learning to solve a diverse array of complex tasks and challenges. The emerging field of federated learning investigates methods to leverage the wealth of data distributed across heterogeneous edge devices to collaboratively train models at scale. Though, there remain open research challenges regarding ensuring data privacy, non-stationary distributions, concept drift, scaling algorithms to large numbers of resource-constrained devices, and practical deployment considerations given real-world requirements.

To this end, we seek an excellent and highly motivated candidate for a Ph.D. position in the area of federated learning with a focus on developing data-centric approaches. The research topics include, but are not restricted to: dealing with multimodality, handling unlabelled data, incorporating human agency, ensuring fairness, adaptive personalization, robustness, and enhancing user-system interplay. For instance, a potential application area for federated learning is in personal health sensing using wearable devices. The digital devices, such as fitness trackers, smart watches, and other biosensors can continuously capture physiological signals like heart rate, activity levels, sleep, and more. They generate substantial amounts of data on a daily basis for each individual user. Federated learning techniques could enable collaborative training of deep learning models on most relevant data across users without compromising privacy.

This Ph.D. position provides the opportunity to advance the frontier of federated learning and distributed sensing systems. The candidate will be supervised by senior researchers and design experts in the Department of Industrial Design at Eindhoven University of Technology and will contribute to its vibrant research community with connections to interesting lines of research in health, mobility and sustainability.

• Master's degree in Computer Science, Mathematics, Machine Learning or a related technical field.
• Strong interest in deep learning with a motivation to apply these techniques to problems in the healthcare domain.
• Ability to work independently and persistently tackle difficult research problems.
• Excellent analytical, problem-solving, and software engineering skills with prior experience implementing machine learning algorithms using well-known frameworks (e.g., PyTorch, TensorFlow, and Flower).
• Aspiration to achieve high-quality research contributions and publications in leading conferences and journals.
• Collaborative spirit and ability to work productively as part of a multidisciplinary team.
• Strong communication skills, including proficiency in written and spoken English and the ability to effectively present technical information to academic and non-expert audiences.
• Interest and ideally experience in design research methods to understand human needs, behaviors, and experiences. Willingness to incorporate user-centered approaches to tackling research problems and evaluating solutions.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities.
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.