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JADS PhD on Challenges and added value of streaming data platforms

Eindhoven University of Technology has a PhD position on Challenges and added value of streaming data platforms, location JADS Den Bosch.
Aanstellingsfunctie(s)
Promovendus
Faculteit(en)
Mathematics and Computer Science
Graduate Program(s)
Computer Science
Instituten en overigen
JADS Den Bosch
FTE
1,0
Sluitdatum
31/03/2021
Aanvraagnummer
V32.4843

Functieomschrijving

Project background and industry involvement

This PhD project will be conducted in collaboration with KPN, a leading telecommunication firm in the Netherlands. Therefore, the practical implications of this research need to be articulated and communicated during the project.

KPN is moving away from a static infrastructure towards a more dynamic infrastructure with streaming data, in which KPN’s Data Services Hub (DSH) will be instrumental. This new set-up of the technological infrastructure has an enormous impact on KPN. Handling large swaths of streaming data does not only require a novel technological infrastructure, but it also has great impact on the organization. First, streaming data typically requires dedicated data mining techniques. Second, streaming data requires different ways of interacting with the customers and employees with more emphasis on shared man and machine decision-making. Third, with streaming data, KPN will add more digital services to their portfolio. This requires new business models to monetize the data. Last, but not least, streaming data typically requires a different way to govern and manage the data. The technical platform should be aligned with KPN’s portfolio of business models and management methods.

Managing platforms

This project focusses on the soft side of streaming data technologies in general and DSH in particular. Increasing numbers and connectedness of sensors, devices and software systems is equally mimicked by the increasing interdependencies between the involved individuals and organizations that make up the network. These are imprinted in business models and are shaping business ecosystems. Similar challenges are observed in the internal processes, starting from aligning different teams to data-driven mindset(s) and new business development.

In this research, we will search for optimal configurations of technology, business models and organizational models, involving the following questions: Which use cases of the DSH are likely to be successful? Which use cases of DSH are likely to have positive (or negative) spill-over effects due to the business and organizational aspects? What are the interdependencies between on-line machine learning algorithms, business models, and organizational models, and how should these be developed and managed? How can we add value to the customers? How should we organize the services on such platforms as DSH accordingly? What skills do we need to be successful and what are the requirements of the organizational culture? What are the optimal roles for KPN in different ecosystems resulting from the European Data Strategy? Examples of horizontal ecosystems are GAIA-X, GSMA, IDSA and ETNO, while vertical ecosystems include Health, Agriculture and City among others.

In this research we will be working with the existing and potential business and use cases of the DSH. Here we try to uncover the soft and hidden secrets of successes and failures of working with machine learning applied to streaming data.

Profile

The research will be conducted under supervision of dr. Ksenia Podoynitsyna.
Our ideal candidate wants to build the bridges between social sciences on one side, and mathematics, statistics, and computer science on the other side. While a healthy understanding of mathematics & statistics is required in this project, it is more important to have a strong understanding of the various strands of social sciences /entrepreneurship and a capability to translate these theories and ideas to statistical and analytical models.

The successful candidate is expected to:

• Perform scientific research in the domain described;
• Present results at (international) conferences;
• Publish results in scientific journals;
• Participate in activities of the group, mainly in 's-Hertogenbosch but also regularly at KPN.

Functie-eisen

Candidates should:

• Have a MSc. in Statistics, Data Science, Computer Science, Econometrics, AI or a related discipline, a Research Master, or Management/Entrepreneurship or a similar Social Sciences degree with a significant quantitative component;
• Have excellent analytical skills;
• Have knowledge of, or a willingness to familiarize themselves with, current research into new and innovative data science techniques such as text mining and NLP;
• Is highly motivated and rigorous;
• Be a fast learner, autonomous and creative, show dedication and be hard working;
• Possess good communication skills and be an efficient team worker;
• The candidate should be able to translate his/her findings into actions. You need to understand the language of the business and formulate your advice in a way that can be quickly adopted by colleagues at KPN.
• Be fluent in English, both spoken and written.

Arbeidsvoorwaarden

The PhD student will be employed at Eindhoven University of Technology.

We offer:

• A full-time position.
• A full-time employment for four years, with an intermediate evaluation after one year.
• A minimum gross salary of € 2.395,- per month up to a maximum of € 3.061,-, 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 TU/e, TiU, 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).

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