Careers

Open Job Positions on Smart Computational Biomanufacturing for Biofoundries


Looking for a PhD in ML, automation and biotech? Think about our opportunities for industrial synthetic biology driven by machine learning and automation.

New work opportunities are now open at a joint initiative between Universitat Politècnica de València and CSIC/I2SysBio/UV on Smart Computational Biomanufacturing for Biofoundries. The goal is to improve the efficiency of the automated Design-Build-Test-Learn (DBTL) cycle for microbial production processes by using machine learning, predictive modeling and optimal design of experiments in biofoundries (Otero-Muras et al. Metabolic Engineering, 2021 https://doi.org/10.1016/j.ymben.2020.11.012, Tellechea-Luzardo et al. Trends in Biotech, 2022 https://doi.org/10.1016/j.tibtech.2021.12.006).

Candidates are expected to contribute to the development of sustainable solutions for bio-based production of therapeutics, food, nutrition and materials; to contribute to the development of standards for exchange of design information and experimental data in close interaction with experimentalists and the development of predictive models through statistical modeling and machine learning. For more information see our publications: https://www.carbonelllab.org/publications.

Candidates will hold a degree in Bioinformatics / Systems Biology / Industrial Engineering / Computer Science / Biomedical Engineering / Synthetic Biology / Biochemistry / Bioengineering / Chemical Engineering (or a related subject area), and willing to learn about postgenomic data analysis, metabolic modelling, statistics, and systems biology.

Interested candidates please send a letter of motivation and your resume to pablo.carbonell@upv.es. Informal enquiries are welcome.

Open positions on Smart Biomanufacturing and Biofoundries

We look for postdoctoral and predoctoral (PhD fellow/student) candidates to work at Universitat Politècnica de València on Smart Biomanufacturing and Biofoundries. The goal is to improve the efficiency of the automated Design-Build-Test-Learn (DBTL) cycle for microbial production processes by using machine learning, predictive modeling and optimal design of experiments (Otero-Muras et al. Metabolic Engineering, 2021 https://doi.org/10.1016/j.ymben.2020.11.012, Boada et al. iScience, 2020 https://doi.org/10.1016/j.isci.2020.101305).

Candidates are expected to contribute to the development of sustainable solutions for bio-based production of therapeutics, food, nutrition and materials; to contribute to the development of standards for exchange of design information and experimental data in close interaction with experimentalists and the development of predictive models through statistical modeling and machine learning. For more information see our publications: https://www.carbonelllab.org/publications.

Candidates will hold or expect to achieve a PhD in Bioinformatics / Systems Biology / Industrial Engineering / Computer Science / Biomedical Engineering / Synthetic Biology / Biochemistry / Bioengineering / Chemical Engineering (or a related subject area), and have technical skills in computer programming and knowledge or willing to learn about postgenomic data analysis, metabolic modelling, statistics, and systems biology.

Interested candidates please send a letter of motivation and your resume to pablo.carbonell@upv.es. Informal enquiries are welcome.