Key Skills

Job Description

Machine learning is increasingly used to support decisions in high-stake domains such as health care, where experts need to understand, verify, and act on what a model has learned. Post-hoc explanations of black-box models offer only a partial view: they approximate a model’s behaviour rather than reveal it. We therefore develop models that are interpretable by construction, such as probabilistic rule sets, subgroup sets, and histogram trees, whose structure, predictions, and uncertainty can be inspected directly. A key challenge is that the same data typically supports many models that perform almost equally well: the Rashomon set. Data alone cannot determine which of these models is most meaningful, plausible, or useful to a domain expert. Meanwhile, experts hold background knowledge that current learning algorithms cannot exploit, and they may wish to steer, constrain, or reject what an algorithm proposes. In this project, you will develop theory and algorithms for human-centered interpretable machine learning: methods that incorporate background knowledge and expert feedback into model learning and selection. The minimum description length (MDL) principle provides a natural foundation by viewing learning as compression. It balances goodness of fit against model complexity without ad-hoc regularization and offers a principled way to encode prior knowledge. Possible directions include knowledge-informed model selection, characterising and navigating the Rashomon set, and interactive learning with experts in the loop. Within this scope there is ample room to shape the research agenda based on your own expertise and ideas. You will be embedded in the Explanatory Data Analysis (EDA) group at the Leiden Institute of Advanced Computer Science. The group develops algorithms and theory that help people discover what matters in complex data, understand cause and effect, and make trustworthy predictions and decisions, building on machine learning, statistics, and information theory. More information about the group can be found at https://eda.liacs.nl/ What You Will Do Conduct independent, fundamental research on human-centered interpretable machine learning, in particular learning with background knowledge and expert feedback; Develop and evaluate methods in collaboration with domain experts, in particular in health care; Publish and present scientific articles in leading journals and conferences; Co-supervise PhD and MSc students; Contribute to teaching activities. Where you will work The faculty is home to more than 2,700 staff members and over 6,000 students enrolled in nine bachelor's and fifteen master's programs. We are located at the heart of the Bio Science Park, one of Europe’s largest science parks, where academia and industry come together. You will be part of the Leiden Institute of Advanced Computer Science (LIACS). LIACS offers the bachelor's programs in Computer Science and Data Science/Artificial Intelligence, as well as master's programs in Computer Science, ICT in Business and the Public Sector, Media Technology, and Science Based Business. According to an independent research assessment, LIACS is one of the leading computer science departments in the Netherlands. We strive for excellence in a supportive institute where academic achievement, enjoyment, and diversity go hand in hand. We offer a clear and welcoming career path for young and talented scientists with the ambition to grow. For more information about LIACS, see http://www.cs.leiden.edu What You Bring A PhD degree (or near completion) in Computer Science, Statistics, Artificial Intelligence, Data Science, or a related field; Excellent knowledge of and experience with machine learning and statistics, in particular probabilistic modelling; Knowledge of and experience with information-theoretic learning (e.g., the MDL principle), interpretable models (e.g., rule sets, trees, subgroups), or Bayesian model selection is a strong plus; Highly motivated to both perform fundamental research in interpretable machine learning and apply the developed methods to real-world applications; Creative, ‘making things work’ mentality, independent, and communicative team player; A track record of publications in leading machine learning or data mining venues and excellent academic writing skills; Excellent programming skills (preferably in Python); Excellent proficiency in English (oral and written). What We Offer An employment contract for the duration of one initially one year. After a positive evaluation of the progress of the research and personal compatibility, the appointment can be extended further two years. This contract falls under the CLA of Dutch Universities; A salary between € 3.706,= and € 5.760,= gross per month, based on a full-time appointment (38 hours, Payscale 10) A holiday allowance (8%), an end-of-year bonus (8,3%), and an attractive pension scheme at ABP; Full reimbursement of public transport commuting costs for home-to-work travel; Flexible working hours: as a standard, you are entitled to a minimum of 29 leave days on the basis of a full-time working week of 38 hours; you can also save for extra leave, for example by working 40 hours a week, and in this way accrue an extra 96 leave hours, or exchange 96 leave hours for a 36-hour week. Lots of options when it comes to secondary employment conditions; we can, for example, discuss options for a sabbatical or paid parental leave. Within our terms of employment individual choices model, you can exchange leave days and/or salary for benefits such as an advantageous sports subscription and bicycle scheme; If your work allows it, hybrid working is possible within the Netherlands; A home-working allowance (day and internet allowance) and attention for good workplaces. The University will also provide you with a laptop. What We Value Promoting an inclusive community is central to Leiden University’s values and vision. We strive to be an open community in which all students and staff members feel valued and respected, and are able to fully develop themselves. We consider an inclusive academic environment, where students and staff can share their experiences and viewpoints, to be essential for the development of talent. Want to apply or find out more? Enquiries can be made to Matthijs van Leeuwen ([email protected]). If you want more information about the procedure, you can sent an email to: ([email protected]) Applications You can apply online via the blue button in the vacancy. Please ensure that you attach the following additional documents: Curriculum vitae; Motivation letter (1 page); (Draft of) PhD Thesis; Contact information for two or three references Only applications received no later than December 11, 2026 can be considered. Applicants may be contacted with requests for additional information and/or exploratory conversations before the closing date.

Core Responsibilities

Conduct independent research and develop theory and algorithms for human-centered interpretable machine learning, incorporating background knowledge and expert feedback. Evaluate methods with domain experts, particularly in health care, publish and present research, co-supervise students, and contribute to teaching.

Requirements

Applicants must hold or be close to completing a PhD in computer science, statistics, artificial intelligence, data science, or a related field, with strong machine learning and statistics experience, particularly in probabilistic modelling. They should have a strong research publication record, excellent academic writing and programming skills (preferably Python), excellent English, and motivation to conduct fundamental research and apply it to real-world problems.

Benefits

• Holiday Allowance
• End-of-Year Bonus
• Pension Scheme
• Public Transport Commuting Reimbursement
• Flexible Working Hours
• Paid Leave
• Sabbatical Options
• Paid Parental Leave
• Sports Subscription Scheme
• Bicycle Scheme
• Hybrid Working
• Home-Working Allowance
• Laptop