PhD Position in Machine Learning for Green Credit Scores

The goal is to resolve how financial settings can adapt practices or propose solutions for designing green credit scoring models. This will facilitate improved access to investment and credit, thereby promoting fair and sustainable economic growth. You will be a member of the MSCA Industrial Doctoral Network on Digital Finance, a European Research and Training programme with several leading European universities and companies involved, such as the European Central Bank and the Bank for International Settlements. You will attend several European PhD courses and participate in extended research stays abroad.

You will join the Industrial Engineering and Business Information Systems (IEBIS) section of the High-tech Business & Entrepreneurship Department (HBE) at the Faculty of Behavioral Management and Social Sciences (BMS).

Background
This PhD position is one four positions at the University of Twente (UT) and one of 19 positions in the context of the international Marie Sk?odowska-Curie Actions project DIGITAL. For the general description of DIGITAL, please check this page. Information about all other positions is available at EURAXESS, if you would be interested in any of the other positions as well, clearly state that in your cover letter.

DIGITAL' main goal? To significantly advance the methodologies and business models for Digital Finance through five interconnected research objectives:

1. Ensure sufficient data quality to supply to the EU's efforts of building a single digital market for data
2. Address deployment issues of complex artificial intelligence models for real-world financial problems
3. Validate the utility of innovative explainable artificial intelligence (XAI) algorithms to financial applications and extend existing frameworks
4. Design risk management tools concerning the applications of Blockchain technology in Finance
5. Simulate financial markets and evaluate products with a sustainability component

The description here focuses on the specific position "Modelling green credit scores for a network of retail and business clients" (mainly included in objective #5), outlining the project's key takeaways and desired skills of future members of the network.

The challenge
In an ever-evolving financial landscape where the process industry responds to the growing demand for sustainable business models, the significance of AI in shaping decision-making cannot be overstated. Using the power of Machine Learning (ML) models, our project will examine financial investments and credit risk indicators. While these AI techniques have found widespread application in traditional financial domains, a noticeable gap exists in addressing the unique dynamics of businesses. Existing financial models often overlook the environmental and social intricacies of credit risk models, elements that can significantly impact access to credit and thereby hamper economic growth.

This Ph.D. project is committed to a comprehensive exploration of the intersection between finance and sustainable business models, with a specific focus on credit risk assessment through the use of AI. Our goal is to provide innovative AI tools and models to businesses and society to promote sustainable financial strategies based on principles of sustainability, fairness, and transparent credit risk assessment. Are you prepared to reshape the financial sector by improving risk assessments and investment choices, all while advocating for sustainability and environmental stewardship? If this resonates with you, this position could be your ideal opportunity!

Requirements:

• You are a highly motivated, enthusiastic researcher who is driven by curiosity;
• You have a master’s degree or equivalent experience in Business, Economics, Engineering, Information Technology, Mathematics, Computational Science or related fields;
• You have a strong passion and outstanding skills in data science and experience working with Python;
• You have knowledge of quantitative modeling of financial markets, econometric techniques, machine learning, or quantitative empirical research methods;
• You have the ability to work on real-world problems in an interdisciplinary and internationally oriented environment;
• You have good communication skills and an excellent command of English.
• You have good programming skills. Experience with machine learning libraries (e.g., TensorFlow, PyTorch) is required.
• You have knowledge and experience with ML design and implementation, working with multiple libraries/platforms (e.g., Pandas, Sklearn, Conda, Github, etc.).
• You have strong mathematical background, in particular statistics & probability, linear algebra, calculus, optimization, and Markov decision processes.
• You have knowledge of financial engineering concepts such as credit risk assessment and mitigation, probability of default, credit portfolio management.
• You have interest in practical applications of ML and AI, with attention to aspects such as explainability, ethics, and honest usage of customers' data.
• You have familiarity with credit risk modeling processes and regulations is a plus.
• The researcher working on this project will be a member of the coordinator team and the doctoral training team, thus contribution to coordination (research, technical, or financial) and other activities of the MSCA Doctoral Network is a substantial element of the candidate's work and professional development plan. We further emphasize that all Ph.D. candidates in DIGITAL will collaborate, therefore involvement and contribution to the topics and activities of DIGITAL is an important element of this position.
• Interested and motivated candidates are encouraged to apply, even when they do not yet possess all desired skills. Through dedicated learning and doctoral training, you will be able to develop relevant skills on the job.

Salary Benefits:

• We encourage high responsibility and independence while collaborating with colleagues, researchers, other university staff and partners.
• We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO).
• Our offer contains: a full-time 4-year doctoral position with a qualifier within 12 months; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within this European network.
• Gross monthly salary of € 2.770 in the first year, increasing each year up to € 3.539 in the fourth year;
• Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• 29 holidays per year in case of full-time employment;
• A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• A green campus with free access to sports facilities and an international scientific community;
• A family-friendly institution that offers parental leave (both paid and unpaid);
• Professional and personal development programmes;
• The university offers a dynamic ecosystem with enthusiastic colleagues and contacts with industry and society;
• A full status as an employee at the UT, including pension, health care benefits, and good secondary conditions are part of our collective labor agreement CAO-NU for Dutch universities.
• This position includes two research stays at industrial partners. First, you will spend 18 months at Swedbank in Vilnius, Lithuania. The second placement is at the European Central Bank (ECB) in Frankfurt, Germany, for four months. During those secondments, you will gain hands-on experience on industry-relevant research projects related to applications of AI in finance. The details of the secondments can change, both the duration and the companies. European mobility is required.

38 - 40 hours per week

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