Doctoral Student SNSF in Quantitative Microbial Ecology and Metabolic Interactions

Requisition
23002
-Posted
10/12/2026
- Type of position (1) - Area of Activity (1) - Rate of Participation (1) - Faculty / Service (1) - Personnel Category (1)

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.

The Pacheco Lab () in the Department of Fundamental Microbiology at the University of Lausanne offers a position as a Doctoral Student SNSF in quantitative microbial ecology and metabolic interactions (interdisciplinary experimental/computational focus).

Expected start date: flexible, earliest 01 January 2027
Contract length: 1 year, renewable
Activity rate: 100%
Workplace:
University of Lausanne, Dorigny Campus

The Pacheco Lab has an open PhD position to investigate how environmental conditions shape microbial interactions, community assembly, and metabolic dependencies, with a particular focus on plant-associated microbial communities.
Microorganisms in nature form complex communities whose properties emerge from interactions among their members. While resource availability strongly influences microbial community composition, we still lack a mechanistic and predictive understanding of how environmental conditions reshape microbial competition, cooperation, and metabolic dependencies.
A major challenge in microbial ecology is that interactions between microorganisms are not fixed, but depend strongly on environmental context. Changes in resource availability, temporal dynamics, and spatial structure can fundamentally alter microbial interactions and community succession. Plant-associated environments provide a particularly interesting setting in which to investigate these processes because of their dynamic resource landscapes and effects on their hosts.
Through this project, we aim to uncover how environmental context rewires microbial interaction networks through metabolic mechanisms. The successful candidate will combine experimental microbiology with quantitative and computational approaches to investigate how resource composition, environmental structure, and temporal dynamics influence bacterial interactions and community assembly.
A central goal is to move beyond pairwise interactions and develop a predictive, network-level understanding of microbial communities, linking metabolic traits and environmental conditions to community-level outcomes. Experimental measurements will be integrated with quantitative analyses and modeling to generate and test ecological hypotheses. A major emphasis will be on developing mechanistic and predictive explanations for community dynamics, rather than solely describing compositional changes.
The project will primarily use controlled experimental systems that capture key ecological features of natural microbial environments, including plant-associated habitats. This work will contribute to a predictive framework connecting metabolism, environmental conditions, microbial interactions, and community succession, with longer-term applications in the design of beneficial microbiomes.

We are looking for a motivated and curious candidate with a strong interest in microbial ecology and quantitative biology. Applicants should have:

• A Master's degree in microbiology, biology, bioengineering, systems biology, quantitative biology, or a related discipline
• A strong interest in microbial interactions, metabolism, community ecology, and community dynamics
• A good foundation in bacterial physiology, metabolism, and cell biology
• Practical experience with microbial culturing and general microbiological techniques
• An interest in quantitative reasoning and data analysis
• Proficiency in English
• The ability and motivation to work both independently and collaboratively within an interdisciplinary research environment

We are particularly excited to hear from candidates who enjoy thinking quantitatively about biological problems and moving between experiments, data, and theory. The following experience would be advantageous, but is not required:

• Programming, quantitative data analysis, or computational biology
• Mathematical, metabolic, or ecological modeling
• Plant-associated microbiology, soil microbiology, environmental microbiology, or plant-microbe interactions

The successful…