Are you interested in applying ideas from causal inference in machine learning research? The Intelligent Data Engineering Lab (INDELab) at the University of Amsterdam is seeking a PhD candidate in causality-inspired machine learning, i.e. the application of ideas from causality to different areas of ML and RL, under the supervision of Dr. Sara Magliacane.

As powerful as today's AI systems are, nearly all of them are only able to see correlations - they can find patterns and apparent relationships in data, and use these patterns to make predictions and decisions. However, correlation is not causation, e.g., an expensive drug may appear to cure a disease until it is discovered that it is only prescribed to patients from more affluent backgrounds that have access to better healthcare.

In a world that is increasingly reliant on AI algorithms for mission-critical decisions, an AI that cannot distinguish between correlation and causation can lead to poor decision making, inefficiency and unfairness. Questions like "Would I have been hired if I was a different gender?" or "Why was my creditapplication denied?" require a fundamental understanding of causality, as does the application of an AI system developed for one context (e.g. a movie recommendation algorithm trained to target a student population) to a different context (e.g. targeting the general public).

What are you going to do

The focus of this PhD position is: how can we use insights from causal inference (a field with an extensive history in statistics, epidemiology and computer science) to improve machine learning and reinforcement learning algorithms? In particular, we are looking for PhD students that are interested in exploring the connections between causal inference, transfer learning and reinforcement learning.

While recent works (e.g. on Predicting Invariant Conditional Distributions) have shown that causal insights may help in identifying features that transfer across different context, even in some apparently hopeless cases - for example, when the new context is substantially different in many aspects from the original context and where there are no examples (labels) in the new context - many of these methods still consider toy examples and leave many open questions on how to implement these insights in a real-world system.

While most ML starts with an abundant set of fairly clean data, one of the aims of INDELab is to tackle machine learning problems in which the data is heterogeneous, noisy, missing or with few labels. The lab is situated within the larger Amsterdam data science and artificial intelligence ecosystem (e.g. Amsterdam Data Science and ICAI) and values practice-informed and interdisciplinary research and outreach.

Your experience and profile:

• Master's degree in Machine Learning, Statistics, Computer Science, Mathematics, or a related field;
• English fluency, both written and spoken;
• Experience in programming and software development, in particular Python, R or C+, and possibly scientific computing or data science tools;
• A passion for fundamental research and theoretical underpinnings of machine learning.

Candidates with a background in causal inference, reinforcement learning, or transfer learning are preferred.

A temporary contract for 38 hours per week for the duration of four 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 four years). This 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, ranges between €2,443 to €3,122 (scale P). This is exclusive 8% holiday allowance and 8,3% year-end allowance. A favourable tax agreement, the '30% ruling', may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities 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 about our extensive package of secondary employment benefits like our excellent opportunities for study and development? 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, 30,000 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 Intelligent Data Engineering Lab (INDELab) investigates intelligent systems that support people in their work with data and information from diverse sources. You will be working closely with Dr. Sara Magliacane who specializes in causality-inspired machine learning and Prof. Paul Groth who works on automated knowledge base construction and data integration. INDElab is strongly embedded in the larger UvA and Amsterdam artificial intelligence ecosystem with strong connections to multiple Innovation Centre for AI (ICAI) labs and the UvA's Data Science Centre.

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