Postdoc in Agentic Systems Biology for Biomanufacturing
Chalmers University of Technology invites applications for a postdoctoral position in Agentic Systems Biology for Biomanufacturing, located in Gothenburg, Sweden. The Division of Systems and Synthetic Biology, within the Department of Life Sciences, is at the forefront of research, innovation, and education aimed at enabling a biobased society and improving human health. The division brings together biologists, chemists, mathematicians, and engineers to quantitatively describe the complex functioning of living organisms and develop technologies for biosustainability and health.
This research project focuses on industrial biotechnology, leveraging large multi-omics datasets and computational models of metabolism to engineer microbial cell factories. The successful candidate will develop intelligent computational systems that integrate constraint-based genome-scale modeling, resource allocation modeling, and agentic methods. The work will involve extending mechanistic models of metabolism to interpret experimental data, building agentic pipelines for autonomous omics data analysis, and developing systems for automated literature mining to ground hypotheses in established scientific knowledge.
The position offers a unique opportunity to advance modeling expertise with state-of-the-art AI methods, addressing high-impact challenges in biotechnology. Key responsibilities include applying and extending computational models to interpret multi-omics datasets, developing software tools for programmatic interaction between large language model agents and metabolic models, designing agentic systems for autonomous data analysis and scientific reasoning, building retrieval-augmented generation pipelines over scientific literature, and benchmarking computational predictions. The postdoc will also publish research in high-impact journals, present at international conferences, supervise master's and PhD students, and may engage in undergraduate/master's teaching.
Applicants must have a doctoral degree in computational biology, bioinformatics, systems biology, computer science, or an equivalent foreign degree by the time of employment decision. Required skills include experience with constraint-based genome-scale modeling, resource allocation modeling, or other computational models of metabolism, hands-on use of tools such as COBRApy, COBRA Toolbox, or RAVEN, machine learning methods applied to biological data, strong Python programming skills, familiarity with omics data analysis, and excellent English communication skills. Preferred qualifications include experience with enzyme-constrained metabolic models, integration of omics data, large language model APIs, agentic LLM systems, retrieval-augmented generation, vector databases, knowledge graph construction, gene regulatory network inference, metabolic engineering, microbial physiology, industrial biotechnology, and open-source research software development. It is highly meritorious if the doctoral degree was obtained within the last three years prior to the application deadline.
The position is a full-time employment for two years, with the possibility of a one-year extension. Physical presence is required throughout the employment, and a valid residence permit must be presented by the start date. Chalmers offers a dynamic and inspiring working environment, employee benefits including healthcare, parental leave, subsidized day care, free schools, and Swedish courses for non-native speakers. The university is committed to gender balance, equality, and inclusion, with initiatives such as the GENIE project.
To apply, submit your application via the online form, including your CV, publication list, teaching experience, and a personal letter outlining your research background and future goals. Applications must be written in English and submitted as PDF files. Incomplete applications and those sent by email will not be considered. The deadline for applications is May 16, 2026. For questions, contact Eduard Kerkhoven, Group Leader in Computational Metabolic Engineering, at [email protected].
