The masTERs course on smArt Agriculture TECHnologies (TERRATECH) project aims to develop an advanced interactive MSc course related to Agriculture IoT Engineering that will train individuals with the necessary skills and knowledge to work in the rising “Smart Agriculture” industry.
The course is also formulated to stimulate transversal competences such as the increased sense of initiative and entrepreneurship. The course is designed to follow the European Credit Transfer and Accumulation System (ECTS) credit standards for certification recognition across the EU.
The novel curriculum consists of interactive teaching methods and partnerships with expert academic and agricultural organizations in order to give to the students a solid background for starting a fruitful career in the industry. The course duration is 8 months, plus a 1-month agricultural (on the job) experience, a month after the end of the teaching period. During the execution of the course 3 mobility periods are programmed. For the first two (14-day each) periods the students and educators from one university will travel to the other and vice-versa, to participate in the large-scale laboratories in order to develop a demonstrator IoT system. Whereas the 3rd period (1 month) is reserved for the agricultural on-the-job training working in smart technology SME’s and in demonstration farms in order to use the developed system. Although the course will be taught in English, local language lessons will be provided to enable the participants to immerge in the local culture during the exchange periods. The course will be open for participation for anyone with a basic agricultural, electrical, mechanical or technical background, such as university students that have completed a suitable bachelor’s degree or professionals with equivalent or higher (5 years minimum) working experience. Priority will be given according to their academic performance or professional experience and in the case of equivalent academic/experience level then participants from less advantaged socio-economic backgrounds (including refugees, asylum seekers and migrants) will be preferred.