General Objectives of the Course

The course aims to promote the upskilling of professionals in the agricultural sector, with a strong focus on sustainability, digitalization, and technological innovation. It seeks to equip participants with the knowledge and practical skills required for the use of bioreactors and biological engineering techniques applied to fermentative processes. Additionally, the course intends to develop competencies in the biotechnological valorization of agricultural products and residues, contributing to more efficient and sustainable production systems. It also introduces advanced methodologies for the monitoring, control, and optimization of fermentative processes, including the application of Process Analytical Technology (PAT) and approaches based on artificial intelligence and machine learning (AI/ML). Finally, the course aims to foster intelligent, innovative, and sustainable management practices aligned with the demands of modern agricultural production.

Admission Requirements

Professionals and degree holders in Agronomy, Environment and Natural Resources, Food Technology, Zootechnics, Animal Science, and related fields within the Agricultural and Biological Sciences.

Candidates should check the admission requirements available on this site, in addition to the information provided here.

Mode of Study

B-learning

Teaching / Evaluation language(s)

Portuguese

Examination Regulations, Assessment and Grading

Course completion is assessed on a Pass / Fail basis, with the possibility of an additional qualitative distinction being awarded.

Learning Objectives and Intended Skills

By the end of the course, participants are expected to demonstrate the ability to operate and monitor bioreactors applied to the biofermentation of agricultural products and residues, as well as to select and handle microorganisms with biotechnological conversion potential. They should also be able to understand and control critical parameters of fermentative processes, apply digitalization tools and Process Analytical Technology (PAT) for process supervision, and use statistical methods alongside introductory concepts of artificial intelligence and machine learning (AI/ML) for process optimization. Furthermore, participants are expected to integrate sustainable, innovative, and technologically advanced practices into modern agricultural production.

Qualification Requirements and Regulations

Academic Regulation of the University of Coimbra