We recently sat down with Petia Radeva, a member of the EIT Deep Tech Talent Initiative’s Advisory Board and a professor at the University of Barcelona, one of our Pledgers, to chat about deep tech, education and the skills for the future.
As well as being a professor in the university’s Department of Mathematics and Computer Science, Petia is also head of the Consolidated Research Group Artificial Intelligence and Biomedical Applications. This is one of 70 consolidated research groups at the university, and is mainly devoted to artificial intelligence, deep learning, machine learning, computer vision, medical imaging, and application to healthcare. She explained: “The university is very proactive in helping young people and researchers exploit deep tech, develop their ideas and bring their solution to market. Acting as an incubator platform for start-ups, it provides mentoring and resources, helps start-ups improve their visibility as well as their daily needs.”
Talking about the future of education and skills required, Petia emphasised the need to establish stronger connections between academia and industry to provide mentorship programmes, access to specialised resources, deep tech start-ups and spin offs. She went on to explain: “We need this platform to bridge the skills gap, so young, deep tech experts and researchers can skill, re-skill, and up-skill in areas they are lacking the relevant skill set.”
For talents looking to up-skill or re-skill, Petia recommends not only keeping up with emerging technologies, but balancing theoretical knowledge with practical skills, stressing that if the theoretical part of deep tech training is not applied to real-world problems, students forget, and they always need to be able to apply knowledge to solve real, practical problems.
“The time when you went to university, learned your subject, and then apply it for the rest of your life is gone. In today’s world deeptech experts work and apply their products to so many different fields and in all these fields we need to continuously acquire new knowledge and skills all the time. Sometimes we call it lifelong learning.”
For young students, Petia advocates the need to be more curious and enthusiastic, proactive, patient, more self-critical, and to go further in acquiring foundational knowledge, because sometimes it’s not easy to see immediately a direct application of the theory. Having a strong background in mathematics, statistics, programming, and software skills is essential to help future deep tech talents solve real problems. Deep tech experts need to have the skills to handle and analyse large data sets that could come from healthcare, social sciences, technology, industrial applications, etc., along with some machine learning and artificial intelligence know-how.
“Technology is constantly evolving. We should be fine-tuned to these developments and skilful enough to learn all of them.”
New opportunities for young researchers and deep tech start-ups and companies, should include mentoring, finding investment, high quality deep tech training, getting experience in practical applications and real-world problem solving, and more.
“What we also need is education in ethical principles. Ethics isn’t just focused on a particular deep tech field, it’s something that needs to be taught across all aspects of deep tech.”
Talking about her motivation to support the EIT Deep Tech Talent Initiative, Petia sees the EIT Deep Tech Talent’s Pledger Community as the right platform with the right skills to identify the common needs and requirements for all the various expertise, groups, and countries to create a roadmap on how to help young, deep tech experts solve the biggest global challenges and in making deep tech accessible to many people. Additionally, she believes the Pledger Community has a huge role to play in improving the visibility and image of various deep tech initiatives and help them propagate success stories across the community.
If you would like to join the EIT Deep Tech Talent Pledger Community, join the Pledge here.