Compositional Hybrid Digital Twins for Fermented Microbial Ecosystems

Edtlab; France; Sur place; EUR 20 000 - 30 000; Plein temps

Avantages offerts par ce poste

Social security

Health coverage

Research travel support

Résumé du poste

Edtlab in France seeks a motivated PhD candidate to contribute to the Engineering Digital Twins program. The research involves developing hybrid digital twin frameworks for microbial fermentation ecosystems.

Responsibilities include conducting innovative research, designing hybrid models, and collaborating with interdisciplinary teams. The position offers a competitive stipend aligned with French standards and various benefits, supporting research and international collaboration.

Qualifications

• Master’s degree in a relevant field is required.
• Strong programming skills in scientific computing environments needed.
• Interest in interdisciplinary research is essential.

Responsabilités

• Conduct research on hybrid modeling frameworks for microbial ecosystems.
• Design and implement hybridization operators for modeling.
• Publish research findings in scientific journals.

Connaissances

Modeling and analyzing complex biological systems

Programming in Python or Julia

Machine learning

Analytical and problem-solving skills

Formation

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

Outils

Python

Julia

Description du poste

Overview

We are seeking a motivated PhD candidate to contribute to the Engineering Digital Twins (EDT) program within Catalyst: the Reliable Hybrid Model Forge. The research focuses on developing hybrid digital twin frameworks for microbial fermentation ecosystems, with an emphasis on compositional hybrid modeling operators and reusable hybridization patterns.

Research Focus

Microbial fermentation systems involve complex interactions between microorganisms, metabolites, and environmental conditions. Mechanistic frameworks such as the consumer–resource model introduced by Niehaus et al. (2019) provide a strong theoretical basis for modeling these systems, capturing resource-mediated interactions such as competition, facilitation, and self-restraint.

However, purely mechanistic approaches may become computationally expensive or insufficient when dealing with high-dimensional experimental data or partially understood processes. Hybrid modeling approaches that combine mechanistic models with machine learning–based surrogate models provide a promising alternative.

This research will investigate how such hybrid models can be constructed through formal operators of model composition, enabling systematic hybridization across modeling paradigms.

The PhD will explore the following research directions:

• Hybridization Operators for Microbial Digital Twins – Designing operators that combine mechanistic ecological models with data-driven components in a consistent simulation framework, for example by learning corrections to partially specified dynamics or by inferring latent processes from incomplete observations.
• Reusable Hybrid Modeling Patterns – Defining compositional patterns that allow recurring hybridization strategies (e.g., surrogate replacement, model augmentation, residual learning, multi-fidelity switching).
• Multi-Fidelity Model Composition – Supporting adaptive switching or blending between models of different complexity, such as consumer–resource models, generalized Lotka–Volterra models, replicator dynamics, and machine learning surrogates.
• Metadata and Interface Standardization – Developing standardized interfaces and metadata structures enabling consistent composition of heterogeneous models within digital twin workflows.

These approaches aim to enable flexible digital twin architectures capable of handling incomplete observations and evolving biological knowledge, where hybrid models can be composed, replaced, or extended using well-defined hybridization operators.

Key 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.

Research Environment

The PhD will be conducted within the Engineering Digital Twins (EDT) research program, which brings together researchers in digital twin engineering, modeling, and data science.

The project will combine expertise in:

• microbial ecology and fermentation science
• ecological and dynamical systems modeling of microbial ecosystems
• digital twin architectures
• hybrid modeling and simulation
• machine learning for scientific systems

Facilities and Resources

• Access to experimental fermentation datasets
• High-performance computing resources
• Collaboration with interdisciplinary researchers in biology, modeling, and digital twin engineering
• Opportunities for international collaborations

Funding and Benefits

• Duration: 3 years
• Salary: Competitive PhD stipend according to French standards
• Benefits: Social security, health coverage, research travel support
• Travel: Support for conference attendance and research collaborations

Qualifications

Required

• 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).
• Background in machine learning and dynamical systems (ODE-based models).
• Strong analytical and problem-solving abilities.
• Good communication skills in English.
• Interest in interdisciplinary research at the interface of biology, modeling, and artificial intelligence.

Preferred

• Knowledge of ecological or biological system modeling.
• Experience with hybrid modeling or scientific machine learning.
• Familiarity with digital twin concepts.
• Experience with experimental datasets.

For scientific questions regarding the PhD topic, please contact the main supervisor directly.