close

PhD Position on designing a recommender system for sustainable investments

Research / Academic
Enschede

During the project, you will closely collaborate with industry and a doctoral training network spread throughout Europe, including extended research stays abroad.

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

Background
This Ph.D. position is one of four positions at the University of Twente in the context of the international Marie Sklodowska-Curie Actions project DIGITAL. For the general description of DIGITAL and the Ph.D. positions, please check https://euraxess.ec.europa.eu/jobs/178873

DIGITAL' main goal? To significantly advance the methodologies and business models for Digital Finance through the use of five interconnected research objectives https://www.digital-finance-msca.com/:

  1. Ensure sufficient data quality to contribute 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 state-of-the-art 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.


This project closely relates to objectives 1, 2, 3, and 5, as we aim to provide a recommender system that improves data quality, applies sophisticated AI technologies to real-world financial scenarios, incorporates explainable AI to increase transparency and trust, and supports the simulation and evaluation of sustainable financial products. Through this, we contribute to the development of a digital single market for data within the EU, aligned with sustainable finance principles.

Recommender systems are well-known information filtering systems that suggest items most relevant to a user. Although recommender systems have been successful in many domains, there are presently none that suggest appropriate investments in sustainable technologies and businesses. This project will develop and deploy a recommender system to inform financial institutions and their clients about investments' sustainability and help to invest in sustainable businesses.

Modern recommender systems are generally a hybrid of content-based and collaborative-based filters through sophisticated algorithmic pipelines, using both item information and user information to get the best out of both worlds. In the financial domain, a key challenge for such hybrid architectures is to ensure regulatory compliance and eliminate potential bias.

The main objective of this research project is to develop a recommender system that applies filters based on both stock data and investor data to offer relevant, diversified, and targeted recommendations that will help propel sustainable investments. In doing so, it is imperative that the system follows regulations and prevents undesirable biases.

Requirements:

We look for a highly motivated, enthusiastic researcher who is driven by curiosity and has/is:

General skills:

  • Master’s degree or equivalent experience in Finance, Economics, Engineering, Information Technology, Computer Science or related fields;
  • A strong passion and outstanding skills in data science and experience working with programming languages such as Python and libraries such as sklearn, and recommender systems libraries (e.g., recmetrics, surprise);
  • Knowledge of quantitative modeling of financial markets, econometric techniques, machine learning, or quantitative empirical research methods;
  • Knowledge of investments and portfolio construction;
  • The ability to work on real-world problems in an interdisciplinary and internationally oriented environment;
  • Good communication skills and an excellent command of English.


Project-specific skills:

  • Knowledge of and/or work experience in the field of investing, ideally in sustainable finance.
  • Good programming skills, primarily in Python. Experience with recommender systems is helpful.
  • Knowledge of data engineering concepts such as machine learning pipelines, product deployment, and data ingestion.
  • Interest in practical applications of recommender systems, with attention to aspects such as explainability and regulation.


Interested and motivated candidates are encouraged to apply, even when not yet possessing 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 fulltime 4-year Ph.D. position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:

  • 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);
  • A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.
  • This PhD position includes two research stays with industrial partners, which likely include the Bank of International Settlements (BIS) and the European Central Bank (ECB). The research stays will comprise 22 months of your PhD. Note that the precise nature of the secondments may be subject to change.
Work Hours:

38 - 40 hours per week

Address:

Drienerlolaan 5