Universiteit Leiden

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Postdoctoral Researcher in Machine Learning, for the Project MAPHSA

Vacancy number
Job type
Academic staff
Hours (in fte)
External/ internal
Placed on
5 March 2024
Closing date
30 April 2024 8 more days to apply

Apply now

The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a:

Postdoctoral Researcher in Machine Learning (1.5 years, 1.0 FTE), for the Project Mapping the Archaeological Pre-Columbian Heritage of South America (MAPHSA).

Leiden University is seeking an enthusiastic and well-qualified postdoctoral researcher in machine learning, in particular classification. The primary objective of this 1.5-year position is to develop a method for identifying endangered heritage sites in forested mountains of South America, and to make contributions to the fields of computer science / machine learning and/or computational archeology.

The position is part of the Mapping Pre-Columbian Heritage in South America (MAPHSA) project. The project seeks to identify, assess preservation, and develop automated methods for detecting threatened archaeological sites, supporting monitoring efforts by local heritage authorities and stakeholders. The candidate will leverage multi-spectral airborne laser scanning and multi-spectral satellite image data to develop machine learning methods for earthwork feature and tree species classification using input features selection, data fusion, pixel/object-based classification, and multilevel classification system methods. Reference data will be derived from legacy data, field sampling, and high-resolution digital/imaging interpretation. Candidates with an interest in extracting complex networks from geospatial patterns, are particularly invited to apply.

During the appointment, the candidate will integrate within LIACS, the computer science department of Leiden University. LIACS has access to a large number of relevant computing facilities and a wide range of expertises. The candidate will be encouraged to collaborate on projects with other members of the Computational Network Science (CNS, https://cns.liacs.nl) group, the MAPHSA project (https://www.upf.edu/web/maphsa), and from other research groups at LIACS in data science, machine learning, and artificial intelligence. There is also the opportunity for the candidate to participate in (limited) BSc, MSc, and PhD student (co-)supervision.

Additionally, there are plenty of opportunities for other types of interaction and collaboration within the project, for example with colleagues from the Department of Geoscience & Remote Sensing at Delft University of Technology (TU Delft), and the Faculty of Archaeology at Leiden University.

Key responsibilities

  • Conduct original and novel research in the field of machine learning and remote sensing technologies;
  • Publish and present scientific work in/at international journals and conferences;
  • Collaborate with other researchers at LIACS, and various international partners in the MAPHSA project;
  • Engaging in organizing activities at the institute, university and potentially nationally, among other things to promote the conducted research;
  • Assist in relevant teaching activities;

Selection Criteria

  • A PhD* in computer science, mathematics, physics, ecology, remote sensing, earth sciences, computational archeology, or a related field.
  • Strong quantitative skills including data analysis, statistical analysis, and data management.
  • Experience in Python programming.
  • An interest in interdisciplinary research spanning the fields of machine learning, archaeology, remote sensing, spatial analysis, and mathematical ecology.
  • Experience in writing and publishing peer-reviewed articles.
  • Fluency in verbal and written English.
  • Ability to work independently and to engage in multi-author collaborative research projects.
  • Interest in working in an interdisciplinary and collaborative environment as a part of a diverse team.

*: excellent candidates not (yet) past the PhD phase, but close to graduation, are also invited to apply.

Research at our faculty

The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are committed to expand fundamental knowledge by curiosity and to look beyond the borders of their own discipline; their aim is to benefit science, and to contribute to addressing the major societal challenges of the future.

The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organised in eight institutes. These institutes offer eight bachelor’s and twelve master’s programmes. The faculty has grown strongly in recent years and now has more than 2.300 staff and almost 5,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together. For more information, see https://www.universiteitleiden.nl/en/science and https://www.universiteitleiden.nl/en/working-at.

The Leiden Institute of Advanced Computer Science (LIACS) is the Artificial Intelligence and Computer Science Institute in the Faculty of Science of Leiden University. We offer courses at the Bachelor and Master of Science level in Artificial Intelligence, Computer Science, ICT in Business, Media Technology, and Bioinformatics. According to an independent research visitation, we are one of the foremost computer science departments of the Netherlands. We strive for excellence in a caring institute, where excellence, fun, and diversity go hand in hand. We offer a clear and inviting career path to young and talented scientists with the ambition to grow. For more information about LIACS, see https://www.cs.leiden.edu

Terms and conditions

We offer a full-time position for a period of maximum 18 months. Salary ranges from € 3.226, - to € 5.090, - gross per month (pay scale 10 in accordance with the Collective Labour Agreement for Dutch Universities).

Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3%), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break.

All our PhD students are embedded in the Leiden University Graduate School of Science https://www.universiteitleiden.nl/en/science/graduate-school-of-of-science Our graduate school offers several PhD training courses at three levels: professional courses, skills training and personal effectiveness. In addition, advanced courses to deepen scientific knowledge are offered by the research school.

D&I statement
Diversity and inclusion are core values of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop their full potential. Diversity in experiences and perspectives enriches our teaching and strengthens our research. High quality teaching and research is inclusive.

Enquiries can be made to Sebastian Fajardo, s.d.fajardo.bernal@liacs.leidenuniv.nl. If you have any questions about the procedure, please contact Anne-Marie Alleblas, email: jobs@liacs.leidenuniv.nl.

Please submit online your application via the blue button in the vacancy. Please ensure that you upload the following additional documents quoting the vacancy number:

  • A short cover letter (1 page) detailing your motivation to apply for the position;
  • A full academic CV including a list of publications. The CV must contain a link to the applicant’s Google Scholar page;
  • The names and addresses of at least two persons that can be contacted for reference (who have agreed to be contacted);

Only applications received before 30 April, 2024 can be considered.

Apply now

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