The Digital Ludeme Project, a five-year research project funded by the European Research Council (ERC), will entail a computational study of the world's traditional games throughout recorded human history. It aims to improve our knowledge of ancient games using modern AI techniques, to chart their evolution and their role in the spread of mathematical knowledge across cultures, thereby establishing a new field of research called Digital Archaeoludology.

The successful applicant will play a leading role in helping to develop the software system and related databases for modelling the expected corpus of over 1,000 traditional games, and preparing the data thus collated for phylogenetic, historical and cultural analysis by the project's "cultural" contingent. The project will produce a new public platform for game AI study, facilitating novel research into machine learning for general game playing, phylogenetic analysis of games including game distance/similarity metrics, and automated strategy learning, transfer and explanation.

The successful candidate will produce publishable results in leading conferences and journals, and will assist with the supervision of PhD students and play an active role in the day-to-day running of the project. This will include coordinating project meetings and field trips, and events such as international symposia, conferences and a public exhibition, and assisting with the pursuit of further research funding.

Applicants should have a PhD in a relevant field (artificial/computational intelligence, machine learning, general game playing, computational phylogenetics, data mining, etc.) and a strong track record of research output and publication. Expert Java programming skills are essential. A strong background in machine learning and experience coordinating research projects and conference-level events would be advantageous. A demonstrated passion for traditional games and an interest in their history will be highly regarded.

Fixed-term contract: 31-03-2023.

The position is offered for the duration of the project (until 31 March 2023) with yearly evaluations. It will be located on-site at Maastricht, The Netherlands, at one of the world’s top new universities known for its international and multidisciplinary focus.

The salary and conditions of employment will be as set out in the Collective Labour Agreement (CAO) of Dutch Universities (www.caouniversiteiten.nl/wp-content/uploads/2017/08/Collective-Labour-Agreement-Dutch-Universities-July-2016-June-2017.pdf) for a full-time contract of 38 hours/week at salary scale 10 (€2,588 to €4,084/gross per month) plus 8% holiday loading and an 8.3% year-end allowance. Non-Dutch applicants may be eligible for a favourable tax treatment (30% rule).

Maastricht University

Established in 1976, Maastricht University (UM) is the most international university in the Netherlands and renowned for its innovative, student-centered problem-based learning system. With approximately 16.300 students and 4.300 employees, Um offers a wide choice of bachelor’s, master’s and PhD programmes, all of which are designed to integrate research and education. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools linked to the university’s six faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Humanities and Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.

Department of Data Science and Knowledge Engineering (DKE)

Founded in 1992 as part of the Faculty of Humanities and Sciences, today DKE broadly covers Data Science and Knowledge Engineering in research and teaching. DKE comprises three internationally respected research groups, having their focus on “Robotics, Agents and Interaction”, “Networks and Strategic Optimization”, and “Biomathematics and Bioinformatics”. Education programmes offered by DKE are a BSc in Data Science & Knowledge Engineering, a MSc in Artificial Intelligence, and a MSc in Data Science for Decision Making. For further information, seehttps://www.maastrichtuniversity.nl/dke