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Postdoctoral Researcher in Data-Efficient Visual Learning

Universiteit Leiden -
Leiden

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Vacaturegegevens

Parttime | Fulltime
€ 3.546 - € 5.538 per maand

Secundaire arbeidsvoorwaarden

Extra vakantiedagen
Pensioen
Sabbatical
Flexibele werkuren
Bedrijfslaptop

Kwalificaties

Doctor
Informatica
PyTorch
Computervaardigheden
Engels
Master
Bachelor
Communicatieve vaardigheden
Python

Volledige vacaturetekst

Job requisition Id: 16728

Job type: Academic staff

Contract hours min: 38

Contract hours max: 38

Location: Leiden

Applying is possible until: 12 augustus 2026

Teaser Text

The Leiden Institute of Advanced Computer Science (LIACS) at Leiden University is seeking a highly motivated Postdoctoral Researcher to join the Machine Learning cluster. The position is part of a research project investigating how visual foundation models can efficiently acquire new expertise in specialised domains where labelled data is scarce. Recent foundation models have transformed computer vision, yet their ability to acquire new expertise remains limited when training data is scarce or specialised. This project aims to develop the next generation of adaptive visual learning systems by enabling foundation models to efficiently specialise through the exploitation of structure within representations and across tasks. The resulting methods will help bridge the gap between general-purpose foundation models and expert-level performance in specialised domains. The successful candidate will work closely with Dr. Hazel Doughty ([email protected]) and will have the opportunity to help shape an ambitious research programme at the intersection of computer vision, machine learning, and foundation models.

Research Focus
The project investigates how visual foundation models can efficiently adapt beyond their original training experience and acquire expert-level capabilities in specialized domains with limited supervision. Potential research directions include, but are not limited to: Adaptation and specialisation of visual foundation models; Fine-grained visual understanding and representation learning; Self-supervised and data-efficient learning; Transfer learning and knowledge reuse across tasks; Structured and compositional learning; Vision-language and multimodal models; Learning with limited supervision; Generalisation beyond pretraining distributions The precise research agenda will be developed together with the successful candidate and may evolve throughout the project.

What you will do

Conduct original and high-quality research in machine learning and computer vision;

Develop novel algorithms for adapting and specialising visual foundation models; Publish research findings at leading machine learning and computer vision venues such as CVPR, ICCV, ECCV, NeurIPS, and ICLR; Present research at international conferences and workshops; Contribute to the supervision of MSc and PhD students; Assist with limited teaching and service activities within the department; Contribute to the development of an internationally visible research programme on fine-grained and data-efficient visual understanding.

Where you will work

Curiosity and passion for science are what connect our people: we seek to know, understand, interpret, and create. Our mission is to provide excellent education and research across the full spectrum of the natural sciences, contributing to solutions for major societal challenges. Our eight institutes—ranging from mathematics, computer science, astronomy, physics, chemistry, and biopharmaceutical sciences to biology and environmental sciences—together with the Lorentz Center and the Hortus botanicus, form the backbone of our faculty and are closely connected. We work in a safe, stimulating, and international environment where enthusiasm and passion drive both research and education, and where building connections and collaboration, both within and beyond the faculty, come naturally.

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, visit our website.

What you bring

We are looking for candidates with: A PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a closely related field; A strong publication record in leading machine learning and/or computer vision venues; Excellent programming skills, particularly in Python and PyTorch; Excellent written and oral communication skills in English; The ability to conduct independent research while working effectively in a collaborative environment Experience with vision-language models, large-scale representation learning, or foundation model adaptation is particularly desirable.

What we offer

An employment contract for a duration of initially one year. After a positive evaluation of the progress of the research, personal capabilities and compatibility, the appointment will be extended by two more years.This contract falls under the CLA of Dutch Universities;
A salary between € 3.546,= and € 5.538,= 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?

If you would like more information about what the job entails, please contact Dr. Hazel Doughty, email: [email protected]. If you want more information about the procedure you can sent an email to: [email protected].

You can apply online via the blue button in the vacancy until 12-08-2026. Please ensure that you upload the following additional documents: Detailed CV; Research statement (max 2 pages); The names and email addresses of two references who can provide letters of recommendation.

Applications will be reviewed on a rolling basis

Information at a Glance

Faculty / Unit: The Faculty of Science

Contact with (Name + position): Dr. Hazel Doughty, Assistant Professor

Contact Email address: [email protected]

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part time student vacatures in delft

Postdoctoral Researcher in Data-Efficient Visual Learning

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