The PhD candidate will work on the development of interpretable deep learning for multi-omics data multi-scale models of the immune system with a focus on investigating cardiometabolic disease (CMD). We are seeking a highly motivated and ambitious PhD candidate for a project focused on developing interpretable deep learning models for the analysis of single-cell and spatially resolved multi-omics data associated with CMD. The PhD candidate will develop models that can automatically identify key molecular and morphological structures, such as clusters of cells, that are essential for making informed predictions. This project aims to enhance the use of multi-omics and spatially resolved data in clinical decision-making, hence, improving treatment guidance and prognosis.
Key Responsibilities:
– Develop deep learning models for the analysis of spatially resolved data.
– Train this model using both publicly available single-cell data in CMD diseases, as well as spatially resolved molecular data generated by MIRACLE partners.
– Focus on model interpretability to identify significant spatial regions and immune profiles.
– Investigate and address uncertainties in AI models, adapting current reliability benchmarks for the analysis of single-cell datasets.
– Contribute to scientific recommendations on AI model robustness.
Expected Outcomes:
– Novel interpretable deep learning models for the analysis of spatially resolved data.
– A novel AI-based classifier of immune profiles.
Requirements
A degree (MSc, or equivalent) in Computational Biology, Bioinformatics, Systems Biology, or a STEM-related field, such as Mathematics, Physics, Computer science, etc. Additionally, a good understanding of Health or Life Sciences, for example, Biology, Microbiology, Molecular Biology, Immunology, Biomedical Sciences, or Biochemistry, will be considered an asset.
Furthermore, the applicant should be able to perform team-oriented as well as independent work.
• Essential skills:
Proficiency in machine learning techniques.
Strong programming skills, preferably in Python.
Solid foundation in mathematical modelling, probability and statistics.
Ability to work collaboratively in an interdisciplinary team.
Good communication skills in English (both written and spoken).
• Good to have:
Experience in computational biology.
Familiarity with multi-omics data analysis.
Knowledge of interpretable deep learning methods.
Good understanding of immunology and/or microbiology.
- The candidate must have a bachelor’s and master’s degree in a relevant discipline.
- Strong interest in pursuing a career in research.
- The candidate must be a highly motivated enthusiastic and efficient researcher.
- Good communication skills in English.
Note that in order to be eligible for this position applicants:
- must not have a doctoral degree at the date of their recruitment
- can be of any nationality
- should be enrolled in a doctoral programme during the project
- should comply with the mobility rules: in general they must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting organization for more than 12 months in the 36 months immediately before their recruitment date.
Benefits
The PhD position is funded under the Marie Skłodowska-Curie Actions (MSCA) framework. The living and mobility allowance for the PhD student is approximately 4,000 EUR per month, in line with EU-MSCA requirements. If applicable, a family allowance will also be provided based on the candidate’s family status assessed at recruitment. Please note that this amount is subject to taxation and deductions for the employee’s national insurance contributions. All payments will be made in euros.
Organization/Company – University of Bern
Research field – Biological sciences » Other
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Computer science » Modelling tools
Research profile – First Stage Researcher (R1)
Country – Switzerland
Application Deadline – 1 Sep 2025 – 22:00 (Europe/Berlin)
More information: Euraxess
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