Assistant/Associate Professor of Machine Learning

Delft University of Technology
July 24 2017
Position Type
Full Time
Organization Type

The growing availability of (big) data is changing every aspect of society. The ability to analyse and interpret the large volume and heterogeneity of data will determine the real impact. Machine learning has become the driving force behind breakthroughs in many applications, such as learning machines to see and robots to walk. Delft University of Technology wishes to further expand its activities in data science by attracting new talent in machine learning. The position is embedded into Delft's Data Science programme (DDS) and within the Intelligent Systems Department (Computer Science) that is devoted to enabling man and machine to deal with the increasing volume and complexity of data, in close cooperation with their environment. You will contribute to and co-lead the research into computational aspects of a variety of machine learning techniques (for example, Deep Learning, Reinforcement Learning, Decision Processes). Application domains within DDS include health, social media, and cyber security. You will act as principal investigator leading day-to-day research activities of your team, acquiring funding, fostering creativity and scientific excellence, and initiating collaborations with internationally leading labs. You will be active in the relevant international communities, and set internationally recognised research directions. You will strengthen the curriculum in machine learning within the Bachelor and Master programmes of Computer Science, by (co)teaching courses such as Pattern Recognition, Machine Learning, Datamining, Computational Intelligence and Deep Learning.

A Tenure Track, a process leading up to a permanent appointment with the prospect of becoming an Associate or full Professor, offers young, talented academics a clear and attractive career path. During the Tenure Track, you will have the opportunity to develop into an internationally acknowledged and recognised academic. We offer a structured career and personal development programme designed to offer individual academics as much support as possible. For more information about the Tenure Track and the personal development programme, please visit

This job comes from a partnership with Science Magazine and Euraxess

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