For more information please contact Prof. dr. Wouter Verbeke, tel.: +32 16 19 34 24, mail: email@example.com.
We offer an employment as full-time doctoral scholar as from now for 1 year, renewable till max. 4 years after positive evaluation.
You can apply for this job no later than January 31, 2021 via the online application tool
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
The research group LIRIS has acquired a solid, world-wide recognized reputation in the field of management informatics and data science, as illustrated by the various top publications in high-quality journals, research projects, presentations at well-respected conferences, the frequent organization of scientific events, and the regular editorial activities undertaken. In the area of data science for business decision-making, the research group has been a leading research center for many years in Flanders, Belgium and Europe. The research team maintains close contacts with other research centers in the faculty, KU Leuven and other universities. LIRIS cooperates with leading international academic research centers in business information systems.
The LIRIS group has extensive links with a network of national and international industry partners, serving either as research sponsors or facilitators for data provisioning and research validation. This cross-fertilization between fundamental research and industry practice allows to conduct research according to the domain’s motto: rigor and relevance.
This research is part of the FWO project ANUBIS: Aligned oNline and multilevelUser and entity Behavior analytics for Intelligent System security :
Digital business thrives by secure transaction processes. Despite advanced authentication procedures and network protocols, a typical organization is estimated by the Association of Certified Fraud Examiners to lose 5% of its revenues due to fraud. Developing powerful fraud detection systems that continuously monitor and learn from data flows therefore is of crucial importance to reduce losses by timely blocking, containing and preventing malicious user behavior. However, like viruses mutate in response to immunity, hackers and fraudsters continuously adapt their methods in response to organizations’ efforts to mitigate fraud. Online systems are relentlessly probed for security vulnerabilities emanating from system modifications and updates. Fraud therefore is dynamic, system-dependent and organization-specific.
Hence, a pressing need exists for adaptive fraud detection systems which on the one hand continuously adapt to system and user behavior evolutions and rapidly learn to detect new fraud patterns from the continuous stream of data that is generated by users and systems, and on the other hand align with the business needs and the organizational environment where the system is deployed. Although in the ideal world the aim of fraud detection and prevention systems is to eradicate fraud, in practice most organizations aim for pragmatic approaches that are cost-efficient in reducing fraud. This can be achieved by aligning fraud detection systems with their actual organizational role and by adapting them to the true objective, i.e., minimizing fraud losses. Therefore, in this project we will be developing systems which allow to optimize security and fraud investigation efforts in function of expected losses. To this end, systems need to learn to detect fraud involving larger losses with higher priority over fraud cases involving smaller losses, taking as well into account the cost of false alarms.
We are looking for a full-time PhD researcher to work on the topic of data-science for business decision-making, with a focus on the development of business-oriented solutions by adopting advanced data-driven methods from fields such as machine learning, artificial intelligence and robust statistics, for application in fraud detection. This research will be carried out in close collaboration with a financial institution and should lead to a PhD-degree.
The research will be conducted as part of a research grant from FWO, co-promoted by Wouter Verbeke, Tim Verdonck and Bart Baesens, in close relationship with other researchers and industrial experts.
The candidate are expected to participate in workshops, seminars and conferences; to be internationally mobile from time to time and to enroll in the PhD program of the Faculty of Economics and Business. The candidate can be asked to assist in the guidance of bachelor projects and master theses, to give exercise sessions for some courses and/or to supervise exams.
We offer a dynamic and pleasant working environment, in a growing trans/multi-disciplinary team that is actively involved in scientific research at the highest international level, combined with a substantial sense of relevance guaranteed by the field work experience with the industrial partners of the team.
The candidate :
- has a master’s degree in any of the following fields or similar: Business Engineering, Applied Economics, Applied Sciences, Engineering, Computer Science, Data Science, Statistics, Mathematics, Informatics, Physics.
- combines strong quantitative and problem solving skills with a profound interest in the development and application of data-driven methods for tackling business problems.
- preferably has: * Knowledge of data analysis methods (statistics, analytics, machine learning); * Programming skills (e.g., Python, R, Java), or is willing to learn; * Strong communication skills and excellent written and spoken command of English.
An average degree of distinction or higher during preliminary studies is required.
Candidates with prior job experience are especially encouraged to apply.