Publisher
source

Gijs Wijnholds

1 month ago

PhD Candidate, Formal Methods in Natural Language Processing Leiden University in Netherlands

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available

Deadline

Jun 26, 2026

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Country

Netherlands

University

Leiden University

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Keywords

Computer Science
Cognitive Science
Information Technology
Deep Learning
Mathematics
Natural Language Processing
Python Programming
Formal Methods
Computational Linguistics
Generalizability
Linguistics
Large Language Models
Machine learning

About this position

Leiden University invites applications for a fully funded PhD position in Formal Methods in Natural Language Processing. This opportunity focuses on leveraging formal methods to enhance the efficiency, transparency, and understanding of Large Language Models (LLMs). While LLMs have achieved remarkable success in various NLP tasks, they often require significant data and computational resources and may lack generalization in tasks where humans excel. The PhD project aims to address these challenges by exploring how formal methods can contribute to developing more efficient and transparent models for Natural Language Understanding.

The research topics are flexible and will be tailored to the candidate’s interests and the expertise of the supervisors, Dr. Gijs Wijnholds and Prof. Dr. Suzan Verberne. Potential areas include logical methods for task-relevant constraints in LLM finetuning, neural-symbolic integration for NLP tasks, probing LLMs’ generalization capacity, evaluating models in ambiguous contexts, and multilingual inference in vision-language models. The candidate is expected to develop their own research line and submit a brief research proposal as part of the application.

The position is based at the Leiden Institute of Advanced Computer Science (LIACS), a leading institute within the Faculty of Science at Leiden University. LIACS offers a vibrant, international research environment with strong connections to artificial intelligence, computer science, and interdisciplinary fields. The Faculty of Science is renowned for its diverse research activities and commitment to personal and academic development, located in the heart of Leiden’s Bio Science Park.

Applicants should have a master’s degree in computer science, artificial intelligence, computational linguistics, logic, or a related field, with substantial knowledge of NLP and deep learning architectures. Strong analytical, research, writing, presentation, and programming skills (Python) are essential. The position offers a full-time contract initially for one year, extendable for three more years upon positive evaluation. The salary ranges from €3,059 to €3,881 gross per month, with additional benefits including holiday and end-of-year bonuses, pension scheme, reimbursement of commuting costs, flexible working hours, options for sabbatical or paid parental leave, hybrid working, home-working allowance, and a university-provided laptop.

Diversity and inclusion are core values at Leiden University, fostering an inclusive community for all students and staff. The application deadline is June 26, 2026. For enquiries, contact Dr. Gijs Wijnholds at [email protected]. Applications must be submitted online and include a motivation letter, CV with publications, a short research proposal, grade lists, examples of scientific writing, code samples (if applicable), and contact details for at least two academic references.

For more information about the Faculty of Science and LIACS, visit www.universiteitleiden.nl/en/science and www.cs.leiden.edu. The direct application link is here.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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