The goal of this project is to develop and evaluate machine-learning campaigns as well as big data architectures that continuously analyze software-defined infrastructures, their qualities, code smells, qualities as well as runtime adaptation possibilities in the scope of DevOps continuous evolution. The PhD student will explore state-of-the-art machine-learning approaches and develop prototypes for the afore-mentioned analysis in the context of sound Empirical Software Engineering research.



The to-be-developed approaches and algorithms will contribute to high-relevance/high-impact research in the context of two EU H2020 Projects focused on the afore-mentioned topics and most notably, serverless computing.



The applications will also be developed by industrial commercial partners in the scope of the afore-mentioned H2020 actions and will include additional functionality providing the user with further industrial data and information from the value-generating industrial context.



The project is a collaboration of the Jheronimus Academy of Data Science (JADS), 's-Hertogenbosch (campus Mariënburg), Tilburg University (TiU), Eindhoven University of Technology (TU/e) and the commercial, industrial and academic partners part of the actions above.



Profile
The research will be conducted under supervision of prof.dr. Willem-Jan van den Heuvel and dr. Damian A. Tamburri. The students are expected to deliver both long-term results (understanding of machine learning in quality evaluation of software-defined infrastructures) and mid-term results (algorithms, approaches, high-impact/high-relevance papers, and best practices).

The successful candidate is expected to:

• Perform scientific research in the domain described;
• Develop software that implements the algorithms described;
• Present results at (international) conferences;
• Publish results in scientific journals;

Participate in activities of the group, mainly in 's-Hertogenbosch but sometimes also in Eindhoven or Tilburg or at one of the commercial partners in several locations in Europe.

Candidates should:

• Have a MSc. in Mathematics, Statistics, Computer Science, Computer Engineering, AI or a related discipline;
• Have a strong interest in machine-learning and deep-learning;
• Have excellent programming skills and be highly motivated, be rigorous and disciplined when developing algorithms and software according to high quality standards;
• Have good technical understanding of the statistical models used in data science and machine learning;
• Have knowledge of, or a willingness to familiarize themselves with, current research into machine learning for software engineering quality evaluation;
• Have a commitment to develop algorithms that analyze Big Data from software-defined infrastructures as well as Big Code;
• Be a fast learner, autonomous and creative, show dedication and be hard working;

The PhD student will be employed at Tilburg - or Eindhoven University.
We offer:

• A full-time position.
• The selected candidate will start with a contract for one year, concluded by an evaluation after approximately 10 months. Upon a positive outcome of the first-year evaluation, the candidate will be offered an employment contract for the remaining three years.
• A minimum gross salary of € 2.266,- per month up to a maximum of € 2.897,-., in the fourth year;
• A holiday allowance of 8% and an end-of-year bonus of 8.3% (annually);
• Researchers from outside the Netherlands may qualify for a tax-free allowance equal to 30% of their taxable salary (the 30% tax regulation). The University will apply for such an allowance on their behalf;
• Assistance in finding accommodation (for foreign employees);
• The opportunity to perform cutting edge research in a large-scale joint data science project involving TiU, TU/e, JADS and a commercial partner and bringing together expertise of several senior researchers;
• Support for your personal development and career planning including participation in courses, summer schools, conference visits, research visits to other institutes (both academic and industrial), etc.;
• A broad package of fringe benefits (including excellent technical infrastructure, savings schemes and excellent sport facilities).