PhD Student in the Distributed Systems Group

Research / Academic

Machine learning has become the core of many of today’s applications, such as self-driving cars and detecting security threats. Due to the large data volume and ubiquitous data sensing from edge devices, e.g., smart phones, machine learning is shifting from the centralized cloud mode to distributed systems where edge nodes can train and update models (a)synchronously. The research objective of this PhD project is to derive scalable distributed algorithms for (deep) machine learning models. The key tasks of this project are: (i) analysing the performance of a wide range of distributed machine learning algorithms on different platformss (ii) exploring hybrid computing models between edge nodes and the cloud, (iii) designing algorithms to optimize the tradeoff between computing and communication of distributed learners, and (iv) designing resource management policies for real-time executions of applications.


 We are looking for candidates who satisfy the following requirements: an MSc degree with excellent results in Computer Science and Mathematics, preferably in distributed systems, theory, or related areas

•              experience in writing python and scala code and conducting scientific evaluations through experimentation

•              solid knowledge in (deep) machine learning algorithms

•              good speaking and writing skills in English  

Salary Benefits:

TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit for more information.

Work Hours:

38 - 40 hours per week


Mekelweg 2