PhD in Quantitative Microbial Ecology & Metabolic Interactions

Unil; Lausanne; Hybrid; CHF 45,000 - 60,000; Full time

Job summary

UNIL invites applications for a Doctoral Student SNSF in quantitative microbial ecology and metabolic interactions. The Pacheco Lab combines experimental microbiology with computational approaches to study plant-associated microbial communities and environmental context effects.

The successful candidate will develop a predictive framework for microbial networks, linking metabolism to community dynamics, with a strong emphasis on quantitative biology and modeling.

Qualifications

• The candidate holds a Master’s degree in microbiology, biology, bioengineering, systems biology, quantitative biology, or a related discipline.
• Strong interest in microbial interactions, metabolism, and community dynamics.
• Good foundation in bacterial physiology and cell biology.
• Experience with microbial culturing and general microbiological techniques.
• Ability to analyze data quantitatively and to work across experimental and computational methods.
• Proficiency in English and ability to work independently and in teams.

Responsibilities

• Investigate how environmental conditions shape microbial interactions and community assembly.
• Combine experimental microbiology with quantitative analyses and modeling.
• Develop mechanistic and predictive explanations for microbial community dynamics.
• Work with plant-associated microbiology and ecological contexts.
• Contribute to a predictive framework linking metabolism, environment, and community outcomes.

Skills

Interest in microbial interactions

Metabolism

Data analysis

Proficiency in English

Independence & collaboration

Microbial culturing

Quantitative biology

Education

Master's degree in microbiology, biology, bioengineering, systems biology, quantitative biology or related discipline

Job description

UNIL invites applications for a Doctoral Student SNSF in quantitative microbial ecology and metabolic interactions. The Pacheco Lab combines experimental microbiology with computational approaches to study plant-associated microbial communities and environmental context effects.

The successful candidate will develop a predictive framework for microbial networks, linking metabolism to community dynamics, with a strong emphasis on quantitative biology and modeling.