Positions in Applied Data Engineering: Big Data Management & GovernanceTo further shape and strengthen our Applied Data Engineering team, we seek enthusiastic colleagues who have completed a PhD degree in applied data/software engineering, information systems, computer science, or a related discipline (e.g., cloud service computing), who hold a strong track record in research demonstrated by internationally recognized publication(s), and who have proven to be excellent lecturers, as witnessed, for example, by (very) positive teaching evaluations.
Since we are particularly high-impact and high-relevance research-oriented, you should be interested to develop application specific solutions in domains such as agro-food, and, safety and security. We refer to this as flavor of data engineering as 'applied data engineering' that ultimately aims at business expansion or entrepreneurial growth. We are thus particularly interested in scholars who develop and experiment with new and creative data-driven and applied data engineering approaches in conjunction with data entrepreneur- or business scholars and practitioners in the before-mentioned domains.
Although we thus encourage applications in all areas of Applied Data Engineering, our group is particularly looking to increase its strength in (1) applying machine-learning solutions to data-pipelines and data lakes, (2) quality and compliance aspects in distributed data-intensive computing, and, (3) digital servitization, edge-computing and IoT. These three areas also pertain to data-driven national and EU projects that currently run, and revolve around entrepreneurial governance, privacy-by-design, and, security and safety in the security and agro food domains.
We solicit applications for an enthusiastic colleague that works on applied Big Data Engineering and Analytics at the level of Assistant Professor. A successful candidate is preferably expected to start as soon as possible.
The profile of the
Applied Big Data Engineering and Analytics assistant professor should match with the following profile desiderata:
- Track record in Big Data, software/data engineering, researching and/or deploying (Big) data and Machine-Learning Pipelines and/or developing Big Code/Process analytics platforms and tools;
- Empirical methods in machine-learning research as applied to software or data engineering, continuous quality maintenance for big code, e.g., code/process smell analytics (quantitative empirical software or data engineering research); and, last but not least,
- Interest and intrinsic drive to experiment with new methods and techniques in application domains such as agrofood, smart manufacturing, and, crime and safety.
The candidate will be involved in courses such as; Advanced ML and Analytics, Engineering for Data Analytics, and, Automated Data Management.