PhD on Multi-criteria, -modal, -score and -optimization Financial Forensics & Analytics (0,8- 1 fte)

PhD on Multi-criteria, -modal, -score and -optimization Financial Forensics & Analytics (0,8- 1 fte)

Published Deadline Location
5 Sep 30 Sep 's-Hertogenbosch

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Job description

JADS is seeking enthusiastic candidates for the position of PhD student in Multi-criteria, -modal, -score and -optimization Financial Forensics & Analytics. On the one hand, multi-criteria decision making is a family of approaches to decision-making and group-oriented negotiation and risk management processes part of combinatorial optimization. On the other hand, financial transactions---from small scale micro-audits to larger-scale cryptomining---require multi-grain inception, representation and reasoning approaches to cope with the sizes, shapes, scale, and volume of transactions to be handled. The candidate shall operationalize this huge ambition by combining the state of the art in hybrid AI with frontier financial analytics, risk modelling and more. Finally, the candidate shall strive to explicitly align our research agenda on AI with the United Nation’s sustainable development goals. The project is funded by Deloitte (The Netherlands).


Short Description 
Given its role in modern financial systems, financial distress is a major phenomenon worthy of considerable interest from several perspectives, especially in the scope of financial informatics and predictive modeling/forecasting of the phenomenon’s predictors as well as its circumstantial scientific ramifications such as automated Fintech Computing or Financial Fatigue assessment. 


To aid in the modelling of financial distress beyond traditional ratio analysis, scores such as Altman’s Z are often used as a reference, but they tend to require inaccessible data or complex predictive scoring exercises by the hand of expensive human analysts. In addition, and probably more problematically, currently applications of perceptron/deep neural networks are typically a-symmetric and tightly coupled with a particular at hand. In particular, they focus on the modelling and analytics exercise only, while neglecting the underlying data pipeline and its automated governance as well as the data productization and discovery opportunities, which remain to this day largely unexplored. That makes such solutions as rather brittle, failing to meet the expectations of business, and exceedingly case-specific in nature. This makes actionable research to resolve these challenges of critical importance. 


Are you an enthusiastic and ambitious researcher with a completed master's degree in a field related to machine learning (Computer science, AI, Data Science) or in Econometrics with an affinity for AI and deep learning? Does the idea of working on real-world problems and with Deloitte excite you? And are you passionate about using trustworthy hybrid AI methods for the next generation of actionable methods and tools that foster a multi-score, multi-criteria, multi-optimization cube capable of offering a multi-modal and explainable overview (M-Cube) over credit risk modelling, scoring, and asset management? 


We are actively recruiting a Ph.D. candidate who will develop and validate novel concepts, methods, and tools for M-Cube capable of delivering a trustworthy overview and understanding over credit risk modelling, scoring, and asset management, and trial them with industrial partners who work with Deloitte.

Job Description
This vacancy falls under the auspices of the JADE lab, which is the data/AI engineering and governance research UNIT of the JADS, and DELOITTE.  In particular, this position will be aligned within the governance of the existing Deloitte lab on Auditing for Responsible AI Software System. This means for example that you will work in a team of 5 other PhD students. 
The industrial setting of the deep involvement of Deloitte will balance the rigour with relevance and ascertain fit with societal requirements and trends, validation with industrial case studies.


Tilburg University


Candidates should:

  • Have a MSc. in Mathematics, Statistics, Computer Science, Computer Engineering, Econometrics, AI or a related discipline;
  • Have a strong interest in data engineering and governance, 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 trustworthiness evaluation;
  • Have a commitment to develop algorithms that analyze Big Data from software-defined infrastructures as well as AI application code;
  • Be a fast learner, autonomous and creative, show dedication and be hard working;
  • Possess good communication capabilities and be an efficient team worker;
  • Be fluent in English, both spoken and written.

Conditions of employment

Fixed-term contract: 4 years.

Being appointed at JADS provides a meaningful job in a dynamic and ambitious community of 2 universities and startups. Based on the project, the candidate receives an appointment at TU/Eindhoven or Tilburg University.
As a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. At Tilburg University you start with a contract of 12 or 18 months with the possibility to extend the contract to a maximum of 4 years in total. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. The minimum gross salary is € 2.541 per month up to a maximum of € 3.247 in the fourth year;


You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.
Preferred starting date: January 1, 2023.

To develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.


Next to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).  
JADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community. 


All researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.



We do cool stuff that matters, with data. The Jheronimus Academy of Data Science (JADS) is a unique cooperation between Eindhoven University of Technology (TU/e) and Tilburg University (TiU). At JADS, we believe that data science can provide answers to society’s complex issues. We provide innovative educational programs, data science research, and support for business and society. With a team of lecturers, students, scientists and entrepreneurs - from a wide range of sectors and disciplines – we work on creating impact with data science. We do this by connecting people,


  • PhD; IT
  • Economics
  • 32—40 hours per week
  • University graduate
  • 21721



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