The Department of Biomedical Engineering at the University of Basel’s Faculty of Medicine is offering exciting research opportunities for both PhD candidates and postdoctoral fellows in computational biology. These positions sit at the intersection of systems biology, mathematical modeling, and pediatric health, with a clear mission: translating complex biological data into actionable healthcare insights. At the core of this work is the Pediatric Disease Modeling Lab, where researchers investigate how early-life exposure to the microbiome shapes immune development and long-term health outcomes. This research contributes directly to advancing pediatric care strategies worldwide.

This is not a routine academic role. It offers: Direct impact on pediatric healthcare Exposure to global datasets across diverse populations Integration of computational theory with clinical application A platform for long-term academic or industry career growth For candidates interested in high-impact computational biology, this is a rare opportunity to work on problems that bridge data science and real-world health outcomes.

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Research Focus: Where Data Meets Pediatric Health

The lab’s work centers on understanding the co-development of the microbiome and immune system using:

• Mechanistic mathematical modeling
• Causal inference frameworks
• Machine learning techniques
• Longitudinal multi-omics datasets

These approaches allow researchers to explore how early-life disruptions influence:

• Infectious disease susceptibility
• Vaccine responses
• Development of non-communicable diseases

The ultimate goal is to translate computational insights into real-world clinical strategies.

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PhD Position in Computational Biology

Role Overview

The PhD position is designed for candidates eager to apply quantitative methods to biological systems within a collaborative and interdisciplinary environment.

You will work closely with leading institutions including:

• Basel Research Centre for Child Health (BRCCH)
• University Children’s Hospital Basel (UKBB)
• Swiss Tropical and Public Health Institute (Swiss TPH)

Key Responsibilities

• Analyze and integrate longitudinal multi-omics data
• Develop and parameterize mechanistic models
• Apply statistical modeling, causal inference, and machine learning
• Collaborate with clinical and experimental researchers
• Contribute to scientific publications

Candidate Profile

Essential Qualifications

• Master’s degree in a quantitative field (Computational Biology, Bioinformatics, Mathematics, Physics, etc.)
• Programming experience (Python, R, or MATLAB)
• Strong interest in biomedical applications
• Excellent English communication skills

Desirable Skills

• Experience in modeling or machine learning
• Familiarity with microbiome or transcriptomics data
• Background in Bayesian or dynamical systems approaches
• Interest in immunology or global health

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Postdoctoral Fellow in Computational Biology

Role Overview

The postdoctoral position targets researchers with strong expertise in statistical modeling and causal inference, focusing on developmental trajectories derived from complex datasets.

Key Responsibilities

• Develop advanced statistical and causal inference methods
• Model microbiome–immune system dynamics
• Apply Bayesian inference and trajectory analysis
• Identify key developmental windows and intervention strategies
• Contribute to publications and grant writing

Candidate Profile

Essential Qualifications

• PhD in computational biology, biostatistics, epidemiology, or related field
• Strong background in statistical modeling and longitudinal data analysis
• Programming proficiency (R, Python, or Julia)
• Excellent scientific writing skills

Desirable Skills

• Multi-omics data integration experience
• Knowledge of Markov models or trajectory methods
• Interest in pediatric or developmental biology

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What the University of Basel Offers

Both positions provide access to a high-impact research environment with:

• Unique international pediatric datasets
• Strong interdisciplinary collaboration
• Career development and mentorship opportunities
• Participation in structured PhD or research programs
• Inclusive, diverse, and supportive academic culture
• Competitive salary (postdoctoral role aligned with Swiss standards)

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Key Research Publications

Applicants are encouraged to review recent work from the lab:

• Tepekule et al., PLoS Biology (2025) – Immune tolerance and microbiome interactions
• Tepekule et al., PNAS (2025) – Probiotic treatments for bacterial decolonization
• Tepekule et al., PNAS (2019) – Antibiotic resistance evolution in gut microbiota

These publications reflect the lab’s strength in combining theoretical modeling with applied biomedical research.

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Application Process

Timeline

• Start date: August 1, 2026 (or by agreement)
• Applications reviewed on a rolling basis

Required Documents

• Cover letter outlining research interests
• Full CV
• Contact details of at least two references
• Applications must be submitted via the official University of Basel: To apply for the PhD APPLY NOW & For the Postdoc APPLY NOW

Contact

For informal inquiries, applicants may reach out to:
Prof. Dr. Burcu Tepekule (burcu.tepekule@unibas.ch).

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Frequently Asked Questions (FAQs)

1. Is the PhD position fully funded?

Yes, the PhD position is fully funded and includes access to institutional resources, mentorship, and structured training programs.

2. Do I need prior experience in biology?

Not strictly. A strong quantitative background is essential, but familiarity with biological data is considered an advantage rather than a requirement.

3. What programming languages are preferred?

Python and R are most commonly used, though MATLAB and Julia are also relevant depending on the project.

4. Can international applicants apply?

Yes, the University of Basel actively encourages applications from international candidates and supports a diverse research environment.

5. What makes this lab unique?

The lab integrates mechanistic modeling, causal inference, and machine learning with real-world pediatric datasets, offering a rare combination of theory and application.

6. Are there opportunities for career advancement?

Absolutely. Both positions emphasize scientific independence, publication output, and networking, providing strong foundations for academic or industry careers.

7. What type of data will I work with?

You will work with longitudinal multi-omics data, including microbiome, immunological, and transcriptomic datasets from global pediatric cohorts.

8. Is collaboration part of the role?

Yes, collaboration with clinical, experimental, and international research teams is a central component of both positions.

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