Postdoctoral Fellow in Modeling and Regulation of Bioproduction

Royal Institute of Technology




Biopharma is a large area that has evolved from production based on bacterial processes to production based on mammalian cells. Today, fed-batch processes are used, but the area is developing rapidly and now integrated continuous bio-production is seen as the next big step. All large companies are now investigating the possibilities of transitioning to this type of process.

This type of process requires feedback regulation, which in turn requires adequate models for the process dynamics. We are currently building up expertise for a world-class research environment around this type of process control. Currently, our team consists of 2 professors, 2 postdoctoral fellows and 1 doctoral student.

This service is linked to the new competency center AdBIOPRO (Center for Advanced BioProduction), which is led by KTH with Lund University and Karolinska University Hospital as partners and seven companies located in Sweden active in the area, including GE Healthcare.

The project is about model-based optimization and regulation of medium and processes. That includes development

of methods for mechanistic macroscopic metabolic modeling, optimization of cellular metabolism and feedback regulation. The project is in close collaboration with the School of Chemistry, Biotechnology and Health at KTH, which is a world leader in perfusion and has extensive lab facilities, as well as our industry partners.

In summary, the project encompasses model development for bioprocesses, the development of data-driven methods (obtained from, for example, data analytics and machine learning) for modeling, optimization and control design. Industrial relevance is achieved through laboratory scale experiments and the use of industrial data.


The applicant must have completed a doctorate or be close to a doctorate in electrical, chemical, biotechnology or equivalent. The doctoral degree must have been completed no more than three years before the last application date. If there are special reasons, the doctoral degree may have been passed earlier (eg in case of illness, parental leave, etc.). Applicants should have a strong background in at least one of the following areas of bioprocess modeling, system identification or model predictive regulation, and experience from at least one of these areas.

The selected candidate needs to have an excellent academic track record, well-developed analytical skills, and problem-solving skills. We are looking for a highly motivated candidate who can work independently. Good knowledge of English in speech and writing is necessary to publish and present research results in international conferences and journals.

We will place great emphasis on personal fitness.

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The application must contain the following:

  • CV including relevant professional experience and knowledge.
  • Copies of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents are not issued in one of these languages. 
  • A brief account of why you want to conduct research, about your academic interests and how they relate to your past studies and future goals; max 2 pages long.
  • Representative publications or technical reports: Documents no longer than 10 pages each. For longer documents (eg dissertations), attach an abstract and a web link to the full text.
  • Contact information for two reference persons. We reserve the right to contact references only for selected candidates.

You apply through KTH's recruitment system. As an applicant, you have the main responsibility for your application being complete when it is submitted.

The application must be submitted to KTH no later than the last application date at midnight, CET / CEST (Central European Time / Central European Summer Time). 


Gender equality, diversity and distance from all forms of discrimination are both a quality issue and an obvious part of KTH's core values.

For information on the processing of personal data in connection with recruitment, read more here.

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