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  Ph.D. position in Finance – Predictive Modeling and Big Data Analytics for Risk Management


   Business School

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  Prof D Erdemlioglu, Prof B Baesens  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

In the context of the joint Ph.D. program between IÉSEG School of Management and KU Leuven’s Faculty of Economics, we invite applications for a Ph.D. position in Finance - Predictive Modeling and Big Data Analytics for Risk Management.

ABOUT IÉSEG SCHOOL OF MANAGEMENT

• IÉSEG is AACSB, EQUIS and AMBA accredited.

• IÉSEG co-leads LEM which is a nationally accredited research center.

• IÉSEG’s faculty includes people from 45 different nationalities with PhD leading universities around the world.

ABOUT FEB

• FEB is a faculty part of KU Leuven which is EQUIS accredited and is consistently ranked among the top-ten European universities in terms of scholarly output and ranked 45th in the 2020 THE overall ranking.

• FEB is committed to excellence in research with nine interdisciplinary research centers and research groups.

• FEB faculty publish regularly in top international refereed academic journals.

QUALIFICATIONS

  • Applicants should have a degree of Master of Science in Quantitative Finance, Econometrics, Financial Econometrics, Financial Mathematics, Engineering, Statistics or equivalent.​​ Mathematics, Engineering, Statistics or equivalent.
  • High level of English proficiency (proof of TOEFL with score of at least 100 on the internet based test and 600 on the paper based test) or IELTS test (minimum score of 7.5) is required. The scores should be obtained no more than two years before the application.​
  • GMAT or GRE scores of no more than five years old. An indication of the minimum score required is 75th percentile​.
  • ​Working knowledge of French and/or Dutch is a plus (but not mandatory).​​​
  • Good knowledge as well as strong interest on programming is strongly recommended.

The Ph.D. student will be supervised by Prof. Deniz Erdemlioglu (IÉSEG School of Management) and Prof. Bart Baesens (KU Leuven) as co-supervisor.

The successful candidate will work on developing new analytical tools as well as models for accurate risk monitoring and prediction. Grounded on the advances in data science (big data, alternative data) and financial technology, the target applications of the research project will cover industry-wide practices pertaining to the credit risk, systemic (cyber) risk, and global market risk that financial institutions are heavily exposed, particularly in the era of COVID-19 pandemic. Aligned with the conditions and expectations of regularity framework, the project flow will aim at reassessing and designing robust stress-stability tests, bringing novel insights to cope with disaster-type extreme financial risk.

WORKING CONDITIONS

The selected applicant is expected to begin in September 2021. Gross salary will be competitive with other European research and academic institutions. The Ph.D. student will be based in Lille (on the site of IÉSEG School of Management) but given the partnership with IÉSEG and KU Leuven, the student will have to spend minimum 1 year in KU Leuven. The first year of the Ph.D. program will take place on the campus of KU Leuven. The Ph.D. student will receive all the means and support to engage in innovative business relevant research projects with high potential to get published in top international peer-reviewed journals (such as Management Science or equivalent).

APPLICATION PROCEDURE

Interested candidates must send their application through this online application form: https://recruitment.ieseg.fr/jobs/979413-ph-d-position-in-finance-predictive-modeling-and-big-data-analytics-for-risk-management?promotion=186408-trackable-share-link-findaphd-predictive-modeling-and-big-data-analytics-for-risk-management.

For any further question, please contact us by e-mail to [Email Address Removed].

APPLICATION DEADLINE

We will begin considering candidates immediately and will continue until the position is filled. We encourage you to submit your application as soon as possible.

Economics (10) Finance (14) Mathematics (25)

 About the Project