Postdoctoral Researcher in Fermentation Modeling and Digital Twins
Develop advanced computational models to optimize microbial fermentation processes using digital twin technology.
Best for:Ideal for computational biologists or bioinformaticians with expertise in metabolic modeling and a strong background in data-driven simulation.
Overview
This postdoctoral position focuses on advancing fermentation modeling by integrating microbial metabolism data with digital twin frameworks. The successful candidate will collaborate with a multidisciplinary team to design, implement, and validate predictive models that enhance bioprocess efficiency and sustainability. Work will span from metabolic pathway analysis to the deployment of real-time simulation tools, with applications in industrial and environmental biotechnology. The role requires strong quantitative skills and a passion for bridging experimental biology with computational innovation.
What you'll do
- Develop and refine computational models of microbial fermentation processes using metabolic and kinetic data
- Design and implement digital twin frameworks to simulate and optimize bioprocesses in real time
- Collaborate with experimental teams to validate models against laboratory and pilot-scale fermentation data
- Integrate multi-omics datasets (genomics, transcriptomics, proteomics) into predictive models
- Publish research findings in peer-reviewed journals and present at international conferences
- Contribute to grant writing and proposal development for future research funding
- Mentor junior researchers and students involved in related projects
- Ensure reproducibility and open-science practices in all computational workflows
Requirements
Degree required: PhD in Computational Biology, Bioinformatics, Biotechnology, Biochemical Engineering, or a closely related field
Language: French and English; proficiency in French is beneficial but not mandatory
Instruction: English
Publications: ≥1 first-author publication in a peer-reviewed journal
- Proficiency in metabolic modeling tools (e.g., COBRA, FBA, kinetic modeling)
- Experience with digital twin development and real-time simulation frameworks
- Strong programming skills in Python, R, or MATLAB for data analysis and modeling
- Familiarity with multi-omics data integration and systems biology approaches
- Demonstrated ability to publish in peer-reviewed scientific journals
- Applicants must hold a PhD degree (or equivalent) in a relevant field
- No nationality restrictions; international applicants are encouraged to apply
Funding & terms
How to apply
Submit a single PDF file containing a cover letter, CV, research statement, and contact details for two referees via email. Include a brief description of your relevant experience in fermentation modeling or digital twin development.
- Cover letter
- CV
- Research statement
- PhD diploma or equivalent
- Two reference letters
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