The Institute of Marine Research (IIM-CSIC) offers 2 job offers for Early Stage Researchers to develop their doctoral thesis within the Marie Skłodowska-Curie Actions – ITN 2021 programme.
The ITN e-MUSE training programme
The E-MUSE training programme aims at developing young researchers’ skills at the interface between artificial intelligence and life sciences. The challenge is to acquire a shared language bridging life science questions and original modelling approaches. The research programme of the E-MUSE network is to develop innovative modelling methodologies to understand a complex microbial ecosystem and identify levers to control and/or predict its evolution. E-MUSE’s transdisciplinary network gathers academic and industrial partners to equip Early Stage Researchers (ESRs) with scientific, research and transferable skills to become leaders in academic research or industry. They will be at the cutting edge of the modelling methodologies that we apply to model structural and dynamic features of microbial communities, to identify key processes and biomarkers for specific applications.
We are offering two job predoctoral positions:
The aim of the project is to understand microbial interactions through time during cheese ripening and how these interactions lead to certain cheese proporties. With that aim we intend to integrate experimental data (growth, external metabolites, environmental factors, etc.) into dynamic ecological models. The plan is to iteratively increase the level of detail of the developed models. In a first stage, we will consider “classical” ecological models, to describe the interactions among microorganisms and environmental components. Subsequently, we will increase the level of detail to include metabolite-mediated relationships (competivite mechanisms, cross-feeding, etc). Ultimately models will be used to formulate and solve dynamic optimizaction problems to compute those environmental conditions that would maximize cheese quality.
The objective of the project is developing a novel control paradigm to guide the modulation of the underlying stochastic gene circuits of bacterial systems that regulate metabolic activity and therefore phenotype heterogeneity through the available environmental stress variables. The central questions to be addressed are, on the one hand, the appropriate system representation for control, which also includes uncertainty compensation for robust tracking and regulation; On the other hand, the technology and inference methods for heterogeneity quantification and monitoring. The proposed control paradigm will be demonstrated on cheese ecosystems, controlling environmental variables towards specific final product characteristics.
How to apply?
Beware, all expressions of Interest must be sent by 31.05.2021, 23:59 Brussels/Paris