PhD on AI-driven multimodal data fusion models in rare diseases

KU Leuven

Vlaams-Brabant

Sur place

EUR 29 000 - 36 000

Plein temps

Il y a 5 jours
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Avantages offerts par ce poste

Holiday pay
Hospitalization insurance
Commuting cost reimbursement
Sports and childcare facilities

Résumé du poste

KU Leuven, Department of Human Genetics, Leuven, Belgium, invites applications for a 48-month full-time PhD fellowship. The project focuses on zero-shot multimodal fusion models for rare disease prediction, integrating spatial and single-cell multi-omics with histopathology and clinical data.

Based in the MSCA SPACE-MEL network, you will develop machine-learning strategies for multimodal representation learning, access to high-performance computing, and secondments in international partner labs;

Qualifications

  • Master's degree in Bioinformatics, Computer Science, AI, Bioscience Engineering or related field, obtained by start date.
  • Applicants must be doctoral candidates, not holding a doctoral degree.
  • Mobility rule: no residence or main activity in Belgium >12 months in 36 months before recruitment.

Responsabilités

  • Develop and benchmark zero-shot multimodal fusion models for rare disease prediction.
  • Integrate spatial and single-cell multi-omics data with histopathology and clinical information.
  • Develop ML strategies for multimodal representation learning.
  • Participate in secondments to partner labs and collaborate across institutions.

Connaissances

Python
PyTorch
Machine learning
Linux
HPC/GPU
Git
Conda/Containers
Snakemake/Nextflow
English

Formation

Master's degree in Bioinformatics, Computer Science, Artificial Intelligence, Bioscience Engineering or related field

Outils

Docker
Snakemake
Nextflow
Conda

Description du poste


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Host institution: KU Leuven, Department of Human Genetics, Leuven, Belgium


Supervisor: Prof. Alejandro Sifrim (KU Leuven, Belgium)


Co-supervisors: Dr. Martin Fergie, University of Manchester, United Kingdom (academic partner); Dr. Samantha Perona, Spotlight Pathology Ltd., United Kingdom (industry partner)


Host Laboratory

The PhD fellow will be based at KU Leuven, in the group of Prof. Alejandro Sifrim. The laboratory specialises in computational biology, artificial intelligence and statistical modelling for the integration of single-cell and spatial multi-omics data. KU Leuven offers a highly interdisciplinary environment, with expertise spanning computational biology, spatial omics and precision medicine.


The host laboratory provides access to advanced computational infrastructure, including GPU- and CPU-based high-performance compute servers and large-scale storage for multimodal data analysis. Through LISCO (Leuven Institute for Single-cell Omics), the candidate will also have access to state-of-the-art single-cell and spatial multi-omics technologies, imaging platforms and integrated bioinformatics resources.


Project

The candidate will develop and benchmark zero-shot multimodal fusion models for rare disease prediction, using melanoma as a use case. The project integrates spatial and single-cell multi-omics data with histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches for multimodal data fusion; and (ii) to extend existing multimodal frameworks with additional omics layers and evaluate their performance across heterogeneous datasets. At KU Leuven, the candidate will develop machine-learning strategies for multimodal representation learning and for the integration of the complex biological datasets generated within the consortium.


Duration and start date: 48 months, full-time. The first 36 months are funded by the MSCA Doctoral Network SPACE-MEL; the appointment is extended to a full four-year doctoral trajectory at KU Leuven. Intended start date: begin 2027.


Eligibility Conditions


  • Master's degree in Bioinformatics, Computer Science, Artificial Intelligence, Bioscience Engineering or a related field, obtained by the start date of the contract.

  • Applicants must be doctoral candidates, i.e. not already in possession of a doctoral degree, and must be admissible to the KU Leuven Doctoral School of Biomedical Sciences.

  • Mobility rule: applicants must not have resided or carried out their main activity (work, studies) in Belgium for more than 12 months in the 36 months immediately before the recruitment date.


Required Skills


  • Strong programming skills in Python, and experience with at least one deep-learning framework (PyTorch preferred).

  • A solid grounding in machine learning. Experience with representation learning, generative models, foundation models or multimodal integration is a strong asset.

  • Experience in the analysis of high-dimensional biological data (single-cell or spatial transcriptomics, digital pathology or biomedical imaging) is an advantage.

  • Comfortable working on Linux and HPC/GPU systems, with version control (git) and reproducible workflows (conda or containers, Snakemake or Nextflow).

  • Able to work independently as well as within an interdisciplinary, international consortium.

  • Proficiency in the English language is required, as well as good communication skills, both oral and written. Successful candidates will need to provide an English test (e.g. IELTS, TOEFL, Cambridge English). You may be exempt if you are a national of a majority native-English speaking country, or have qualifications / degree that has been taught and assessed in English. The supervisor may also confirm that a candidate has the required level of English.


Secondments

Secondments: The project is carried out in close collaboration with the groups listed below, and visits to their laboratories are part of the training programme. These secondments take place within the contract period; a willingness to travel and to spend extended periods abroad is therefore essential.



  • 4 months at University of Manchester, United Kingdom (supervisor: Dr. Martin Fergie)

  • 3 months at Spotlight Pathology Ltd., United Kingdom (supervisor: Dr. Samantha Perona)


Offer

The doctoral candidate will receive a competitive salary in accordance with the MSCA Doctoral Networks program, comprising a living allowance and a mobility allowance. A family allowance is foreseen where applicable.


KU Leuven additionally offers holiday pay, hospitalization insurance, reimbursement of certain commuting costs and access to its sports and childcare facilities.


Required Documents


  • Statement of interest (limit of 2,500 characters) explaining why you wish to be considered for the fellowship and which qualities and experience you will bring to the role.

  • Curriculum vitae.

  • A certificate of University examinations taken (with marks).

  • A final degree certificate translated in English. If, at the time of application, candidates should not be yet in possession of a degree certificate, they can submit it at the time of the examination. However, if successful, the candidate will be required to provide a translated and legalised certificate as proof of eligibility for doctoral admission.


A limited number of applicants will be invited for an interview and will be asked to provide the contact details of up to two referees.


You can apply for this job no later than October 15, 2026 via the online application tool


KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.



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