Erdal Kayacan holds a Ph.D. in electrical and electronic engineering from Bogazici University, Turkey. After a post-doc at  KU Leuven‘s division of mechatronics, biostatistics and sensors (MeBioS), he worked in  Nanyang Technological University  at the  School of Mechanical and Aerospace Engineering. Currently, he is associate professor at  Aarhus University, Department of Engineering, where he leads the Artificial Intelligence in Robotics (Air Lab) laboratory. Dr. Kayacan is co-author of a text book “Fuzzy Neural Networks for Real Time Control Applications, Concepts, Modeling and Algorithms for Fast Learning“. He is  an Associate Editor of  IEEE Transactions on Fuzzy Systems  and  Technical Editor of the  IEEE/ASME Transactions Mechatronics.

Einar Broch Johnsen, professor of computer science at University of Oslo, expert on formal methods and programming language theory. He has experience with modeling languages (including the design of the ABS language), formal semantics, software evolution, actors and distributed systems, virtualization and cloud systems, deductive verification, executable models and symbolic execution. In REMARO, Einar will supervise projects in formal methods and semantics for AI in robotics systems.

João Tasso de Figueiredo Borges de Sousa is professor at the Electrical and Computer Engineering Department at Porto University and the head of Underwater Systems and Technologies Laboratory. His research interests include autonomous underwater, surface and air vehicles, planning and execution control for networked vehicle systems, optimization and control, cyber-physical systems, and applications of networked vehicle systems to the ocean sciences, security, and defence. He received BES Innovation National Award (2006), an outstanding teaching award from Porto University (2008) and IEEE Ocean Engineering Society mid-career Rising Star award (2018).

Bilal Wehbe is a researcher at DFKI Robotics Innovation Center. He holds a PhD from the Faculty of Mathematics and Computer Science at the University of Bremen, a Masters in Mechanical Engineering from the American University of Beirut, and a Diploma in Mechanical Engineering from the Lebanese University. His PhD thesis involved the use of Machine Learning to identify the dynamic models of underwater robots and adapt them to changing environmental conditions, detecting and recovering form possible failures that might occur during autonomous underwater missions. His research interests include Robot Model Identification and Control, Reinforcement Learning, Life-long Machine Learning, and their applications to Underwater Robotics. Mr. Wehbe was a fellow of the Marie Curie ITN project ROBOCADEMY. Bilal has more than 5 years of professional experience as a robotic research engineer and more than 20 peer-reviewed publications.