진학프로석·박사 채용 접수 플랫폼

Scientific context

The transition toward continuous production processes for recombinant proteins represents a major challenge in order to reduce production costs, improve robustness, and limit environmental footprint. Within the PRODIGES project, our team is developing a multi-criteria decision-support tool to compare production strategies (batch, continuous, hybrid) by integrating economic, environmental, and operational indicators.

This postdoctoral project aims to strengthen this tool through the integration of advanced hybrid artificial intelligence (AI) approaches, in order to improve predictive capabilities, uncertainty management, and the transparency of decision-making processes.

Project objectives

The postdoctoral researcher will contribute to the development of an intelligent digital twin of the production chain (from laboratory scale to industrial scale). The main tasks will include:

• • Developing hybrid models combining phenomenological models (mass and energy balances, biological kinetics) with data-driven models (machine learning, Bayesian networks, Gaussian processes).
• • Implementing multi-objective optimization methods integrating economic criteria (CAPEX, OPEX, production costs), environmental criteria (water and energy consumption, carbon footprint), and operational criteria (robustness, flexibility, contamination risks).
• • Developing probabilistic approaches for uncertainty management (biological, technological, and market variability).
• • Designing a prototype interactive decision-support tool integrating explainable AI techniques to facilitate its adoption by process engineers and industrial stakeholders.