Are you interested in doing a PhD in Explainable AI (XAI) with applications on financial services? Do you want to make a substantial scientific contribution to field of algorithmic fairness while being part of the University of Amsterdam's new AI4Fintech hub?

At the Socially Intelligent Artificial Systems (SIAS) group of the Informatics Institute (IvI), and the Cognition, Language and Computation (CLC) lab of the Institute of Logic, Language and Computation (ILLC), we are interested in both advancing fundamental AI research and developing novel solutions for concrete problems from the Fintech Industry; targeting, in particular, explainability and fairness. As a part of this research, we are seeking a highly motivated PhD candidate, with a strong computational background and curiosity and passion for scientific research, to be part of these groups, and to help us push the boundary of explainability and fairness in algorithmic decision making, together with the Fintech industry.

Why is this research important?

Numerous decisions impacting our society are, nowadays, taken by increasingly complex machine learning algorithms. A considerable amount of these decisions are in the domain of finance. Financial institutions are relying their decisions more and more on machine learning algorithms. Yet the more complex the machine learning models, the more difficult to explain them, at least within the capabilities of the current architectures. This, in turn, impedes the understanding of the decisions taken by these models and hampers fair impact of algorithmic decision making in finance, for example on different minority groups.

A promising direction in tackling the challenges with increasingly complex machine learning algorithms, lies in neuro-symbolic approaches, which the AI community has been putting an increasing emphasis on recently. By combining the strengths of machine learning algorithms, such a deep neural networks, with the strengths of symbolic approaches, you will develop more transparent, explainable and fair decision making models; for example, for fraud-detection, uncertainty quantification in credit assessments, stock market predictions and insurance risk metrics.

What are you going to do?

You will be integrated in a highly stimulating research environment, both multi-disciplinary and international, and are expected to:

• investigate empirical and theoretical properties of explainable and fair AI models;
• develop improved AI models for explainable and fair decision making;
• collaborate with researchers within SIAS group and CLC lab;
• interact with other academic and industrial partners in the AI4Fintech hub;
• present and publish results in scientific workshops, conferences, proceedings and journals;
• participate in and report progress at meetings with AI4Fintech stakeholders;
• assist in relevant teaching activities;
• complete and defend a PhD thesis within 4 years.

Your experience and profile:

• A Master's degree in Artificial Intelligence, Logic, Operations Research, Mathematics, Computational Physics or a related discipline;
• Background in machine learning and data science or interest in the fields;
• Experience or ability to quickly gain knowledge in knowledge representation formalisms (discrete or probabilistic) including causality;
• Good programming skills (e.g., Python, Julia, R, C/C++, Mathematica);
• Good knowledge in statistics, basic probability theory and linear algebra;
• Strong communication, presentation and writing skills and an excellent command of English;
• Socially engaged, highly motivated and curious to tackle new problems in the field.

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The preferred starting date is March 2023. Your work should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The gross monthly salary, based on 38 hours per week and dependent on relevant experience, ranges between € 2,541 in the first year to € 3,247 in the last year (scale P). UvA additionally offers an extensive package of secondary benefits, including 8% holiday allowance and a year-end bonus of 8.3%. The UFO profile PhD Candidate is applicable. A favourable tax agreement, the '30% ruling', may apply to non-Dutch applicants. The Collective Labour Agreement of Universities of the Netherlands is applicable.

Besides the salary and a vibrant and challenging environment at Science Park we offer you multiple fringe benefits:

• 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January.
• Multiple courses to follow from our Teaching and Learning Centre.
• A complete educational program for PhD students.
• Multiple courses on topics such as leadership for academic staff.
• Multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses.
• 7 weeks birth leave (partner leave) with 100% salary.
• Partly paid parental leave.
• The possibility to set up a workplace at home;
• A pension at ABP for which UvA pays two third part of the contribution.
• The possibility to follow courses to learn Dutch;
• Help with housing for a studio or small apartment when you're moving from abroad.

Are you curious to read more about our extensive package of secondary employment benefits, take a look here.

Faculty of Science

The University of Amsterdam is the Netherlands' largest university, offering the widest range of academic programmes. At the UvA, 42,000 students, 6,000 staff members and 3,000 PhD candidates study and work in a diverse range of fields, connected by a culture of curiosity.

The Faculty of Science has a student body of around 8,000, as well as 1,800 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

The mission of the Informatics Institute (IvI) is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.

The Institute for Logic, Language and Computation (ILLC) is the leading research institute in the interdisciplinary area dealing with the investigation of information, information exchange, and cognition with formal and mathematical methods. It is deeply rooted in the Amsterdam logic research tradition dating back to the early twentieth century and covers the disciplines of mathematics, computational linguistics, linguistics, computer science, philosophy and artificial intelligence with staff members from the Faculty of Science and the Faculty of Humanities of the University of Amsterdam.

The Socially Intelligent Artificial Systems (SIAS) group focuses on civic-centred and community-minded artificial intelligence (AI) that aims to reduce inequality and promote equal opportunity in society. The predominant question the group tries to answer: How can we use AI, and in particular learning systems, to advance society? And how can we do that in such a way that people from all corners of society benefit from it? SIAS positions itself in the AI and Data Science research themes of the Informatics Institute, with clear links to the Computational Science and Systems & Networking themes.

At the Cognition, Language and Computation (CLC) lab we investigate topics at the intersection of computational linguistics and cognitive science. We try to understand the computational principles underlying natural language understanding by humans and machines. We investigate the neural implementation of these principles in the human brain, their evolutionary origins and their usefulness in language technology. Important research areas are Iterated Learning, Parsing, Recursive Neural Networks, and Artificial Language Learning.

Want to know more about our organisation? Read more about working at the University of Amsterdam.

Any questions?

Do you have any questions or do you require additional information? Please contact:

• E: Erman Acar, Assistant Professor.