Postdoctoral positions in predictive modelling of antibiotic resistance spread

Introduction

UNIL is a leading international teaching and research institution, with over 5,000 employees and 17,000 students split between its Dorigny campus, CHUV and Epalinges. As an employer, UNIL encourages excellence, individual recognition and responsibility.

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Presentation

We are looking for two postdoctoral researchers to join our team at the intersection of evolutionary theory, microbial ecology and public health. We use mathematical and statistical modelling to draw insights from genomic and epidemiological data; address problems in public health; and answer fundamental questions about bacterial ecology and evolution.

Join us in developing approaches to predicting the spread of antibiotic resistance at the host population scale. We are interested in translating fundamental evolutionary insights into robust predictions about the real-world behaviour of resistance frequencies. We are looking for researchers to collaboratively shape this project through several possible directions:

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  Epidemiological modeling: Developing hybrid mechanistic-statistical models of resistance dynamics.

  
  


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  Data-driven prediction: Using statistical modeling, causal inference, or machine learning to forecast resistance trends.

  
  


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  Fitness estimation: Inferring the fitness impacts of resistance determinants using surveillance and genomic data.

  
  

The Evolutionary Epidemiology group is based at the University of Lausanne’s Computational Biology Department. We are also affiliated with the NCCR Microbiomes, providing fantastic opportunities for collaborations with experimental, clinical and other modelling groups. The group is very friendly, with people from diverse backgrounds working on a range of projects relating to microbial evolution and dynamics. 

We’re looking for curious, collaborative and thoughtful researchers. The envisioned topic is antibiotic resistance, but there is some flexibility: if you are interested in other aspects of evolutionary epidemiology, don’t hesitate to get in touch. If you like our papers, we want to hear from you!

Job information

Duration: 2 years (initial contract for 1 year, but funding is available for 2 years)

Potential for further extension depending on funding

Start date: ideally September 2026, but there is flexibility in both directions

Workplace: University of Lausanne, Dorigny 

Your qualifications

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  A PhD in a relevant topic, such infectious disease modelling, computational biology, physics, or statistics.

  
  


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  Expertise in either mathematical modelling (including model fitting), statistical modelling or machine learning.

  
  


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  An interest in public health and antibiotic resistance

  
  


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  Excellent written and oral communication skills in English.

  
  

What the position offers you

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  An exciting opportunity to shape your research in a supportive and stimulating environment.

  
  


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  Exceptional scope for collaboration through e.g. the NCCR Microbiomes network. 

  
  


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  Access to top-notch resources and possibilities for independent funding and fellowships.

  
  


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  Excellent support for further learning and professional development.

  
  


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  Plenty of opportunities to attend conferences and to build your scientific network.

  
  


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  Attractive salary, beautiful location and excellent quality of life in a great city.

  
  

Your application

Deadline :  31.5.2026

Please, send your full application in Word or PDF.

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  A cover letter (1 page) outlining your background and experience, expertise, research interests and motivation for applying for the position.

  
  


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  A copy of your most relevant paper or preprint.

  
  


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  A CV including a list of publications and contact details of two referees.

  
  

Only applications through this website will be taken into account.
We thank you for your understanding.

For informal enquires, contact [email protected]

Additional information

UNIL is committed to:
•    equality, diversity and inclusion within its community;
•    ensuring an open and respectful environment that is conducive to personal development;
•    offering working conditions that facilitate work-life balance;
•    supporting early career researchers.

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