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Ruud Barth's work experience includes being the CEO and Founder of Saia Agrobotics since September 2017. Prior to that, they were a Researcher and Project Leader for Computer Vision & Robotics at Wageningen University & Research from September 2012 to April 2019. Ruud has also worked as a Visiting Scholar at Caltech in January 2018, and at Harvard University - SEAS Biorobotics Laboratory in July 2016, October 2016, September 2014, and December 2014. In addition, they were a Visiting Scholar at Stanford University - CCRMA in September 2011, and an Intern at Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging from September 2008 to August 2009. Ruud Barth has also held various roles at Radboud University Nijmegen, including serving as a Master Member & Officer of Communication of Degree Program Committee A.I. from September 2010 to August 2011, and as a Student Assistant for the Master Course: Evolutionary Algorithms from September 2009 to February 2010. Ruud was also the President of Study Association CognAC from September 2008 to August 2009.

Ruud Barth's education history is as follows:

From 2015 to 2018, Ruud Barth attended Wageningen University & Research, where they pursued their PhD in Artificial Intelligence for Robotics in Agriculture.

In the period of 2009 to 2011, Ruud Barth studied at Radboud University, earning a Master of Science (M.Sc.) degree in Artificial Intelligence.

Prior to that, from 2006 to 2009, they obtained their Bachelor of Science (B.Sc.) degree in Artificial Intelligence from Radboud University.

In their earlier education, from 2000 to 2006, Ruud Barth attended Kruisheren College and completed their Atheneum degree with a Nature & Health profile.

Additionally, in 2013, Ruud Barth participated in the ICVSS 2013 - International Computer Vision Summer School, where they studied Computer Vision & Machine Learning. However, no degree was specified for this program.

Ruud Barth also holds a certification from Coursera Course Certificates. Ruud completed the course "Neural Networks for Machine Learning - University of Toronto on Coursera" and achieved a score of 92.4%. The specific month and year of obtaining this certification were not provided.