Development of intelligent applications running advanced machine learning models on the edge of the network such as mobile, smart home, internet-of-things platforms require connectivity and computing resources. As the complexity of the applications grow (i.e. streaming video processing, virtual/augmented reality, mobile solutions) the computer network infrastructure between the point of service and the compute infrastructure is becoming a bottleneck. With the advancements of hardware at the edge of the network, where the actual service is needed, the possibility to move some or most of the computational load is becoming evident.

Thus, we aim to study methods for extending deep neural network models from the core of the network into models that exist both on the edge and on the core. The main practical goal is formulated as: taking into account network factors such as available resource on the edge, connectivity, bandwidth, latency and jitter, provide reliable, efficient and scalable Deep Learning based solutions to a multitude of connected devices. To achieve this goal, we will study the issues of DLCE reliability, efficiency, scalability and improved experience.

This project is in collaboration with KPN, a Dutch landline and mobile telecommunications company. DLCE is part of a larger project TU/e-KPN flagship SmartONE, which constitutes an interdisciplinary collaboration between KPN and 4 TU/e research centers: Smart Cities, Wireless Technology, Photonics Institute and Data Science.

We are looking for candidates that meet the following requirements:

a solid background in Computer Science with specialization in deep learning, machine learning, data mining or related areas (demonstrated by a relevant Master project);

have a strong interest in deep learning research;

have machine learning/data mining software development skills at least in one language, e.g. R, Python, Java; familiarity with deep learning libraries and Python is a plus.

good communication skills in English, both in speaking and in writing (candidates from non-Dutch or non-English speaking countries should be prepared to prove their English language skills);

capability and willingness to work both independently and in a team of data scientists and interact with domain experts; being highly motivated, rigorous, and disciplined.

PhD students are expected to:

perform scientific research in the domain described

collaborate with other researchers in this project, transfer knowledge to internal specialists at KPN and assist in guiding (project-related) MSc thesis projects

participate in doctoral training on relevant topics

present results at leading international conferences in the field

publish results in scientific journals

be willing to work at two locations (TU/e campus in Eindhoven and KPN office in [Amsterdam/The Hague)

We offer:

A full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months;

A gross salary of €2.222 per month in the first year increasing up to €2.840 in the fourth year;

Tight collaboration of academia with industry with access to real data and domain expertise.

Strong collaboration ties with several research groups in Europe and world-wide.

Healthy travel funding for presenting your work at the leading conferences and for visiting research.

Support for your personal development and career planning including courses, summer schools, conference visits etc.;

A broad package of fringe benefits, e.g. excellent technical infrastructure, child daycare and excellent sports facilities, extra holiday allowance (8%, May), and end-of-year bonus (8.3%, December).