Hybrid Digital Twin for Fermented Microbial Systems

Engineering Digital Twins (EDT); France; On-site; EUR 20,000 - 27,000; Full time

Benefits offered by this job

3-year duration

Competitive PhD stipend

Social security & health coverage

Research travel support

Job summary

Engineering Digital Twins (EDT) invites applications for a PhD candidate to develop hybrid digital twin frameworks for fermentation ecosystems, focusing on compositional modeling operators and reusable hybridization patterns.

The project combines mechanistic ecology with machine learning and utilizes experimental datasets from vegetable fermentation to calibrate and validate models within the EDT program.

Qualifications

• Master’s degree in Computer Science, Computational Biology, Applied Mathematics, or related field.
• Strong interest in modeling and analyzing complex biological or dynamical systems.
• Programming skills in scientific computing environments (e.g., Python, Julia, or similar).

Responsibilities

• Conduct research on compositional hybrid modeling frameworks for digital twins applied to microbial ecosystem modeling.
• Design hybridization operators integrating mechanistic and data-driven models.
• Develop reusable hybrid modeling patterns for microbial ecosystem simulation.
• Develop and implement mechanistic models of microbial fermentation dynamics (e.g., consumer–resource models).
• Implement prototype digital twin architectures for fermentation systems.
• Develop methods for parameter inference and model calibration from fermentation time-series data.
• Publish research findings in leading conferences and journals.
• Participate in interdisciplinary collaboration within the EDT program.

Skills

Python

Machine Learning

Dynamical Systems

English communication

Education

Master's degree in Computer Science, Computational Biology, Applied Mathematics, or related field

Tools

Python

Julia

Job description

Engineering Digital Twins (EDT) invites applications for a PhD candidate to develop hybrid digital twin frameworks for fermentation ecosystems, focusing on compositional modeling operators and reusable hybridization patterns.

The project combines mechanistic ecology with machine learning and utilizes experimental datasets from vegetable fermentation to calibrate and validate models within the EDT program.