Wallenberg AI, Autonomous Systems and Software Program
Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest ever individual research program, a major national initiative for strategically motivated basic research, education and faculty recruitment.
The program addresses research in AI and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. Software is the main enabler in these systems, and is an integrated research theme of the program. The vision of WASP is excellent research and competence inAI,autonomous systems and software for the benefit of Swedish industry.
The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of AI, autonomous systems and software. The curriculum provides the foundations, perspectives, and state-of-the-art knowledge in the different disciplines taught by leading researchers in the field. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry.
WASP Postdoctoral Positions in Autonomous Optimization
- One PostDoc at Linköping University, Linköping, Sweden
- One PostDoc at KTH Royal Institute of Technology, Stockholm, Sweden
Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program, and provides a platform for academic research and education, fostering interaction with Sweden’s leading companies. The program addresses research in artificial intelligence, autonomous systems and software as enabling technologies for development of systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems of systems. The program is conducted in close cooperation between leading Swedish universities with an aim to promote the competence of Sweden as a nation within the area of AI, autonomous systems and software, http://wasp-sweden.org/ .
Autonomous Optimization is one out of seven WASP Expedition Projects (high gain/high risk targeted projects with a specific challenging goal). The research will focus on development of autonomous optimization, i.e. how to use tools from machine learning to automatically design and tune optimization algorithms. In particular, we plan to investigate how to learn new efficient distributed optimization methods suitable for decision-making in autonomous systems and AI/ML. The main research challenges of the project are in the intersection between optimization, control and machine learning. This is a collaborative project between the Optimization Group headed by Professor Anders Hansson within the Division of Automatic Control at the Department of Electrical Engineering at Linköping University and the Department of Automatic Control headed by Professor Bo Wahlberg at KTH Royal Institute of Technology, Stockholm, Sweden.
The Division of Automatic Control at Linköping University conducts research, education and industry/society interplay within optimization for control, robotics and autonomous systems, sensor fusion and system identification. It consists of ten faculty, 8 adjunct faculty, 2 postdocs and over 25 PhD students.
The Division of Automatic Control at KTH Royal Institute conducts research, education and industry/society interplay within modelling, identification, control, learning and optimization of dynamical systems. It consists of ten faculty, 20 post-docs and over 50 PhD students.
We are offering two two-year postdoctoral positions, one at Linköping University and one at KTH Royal Institute of Technology. Successful candidate will be able to join the WASP research network with over 150 active PhD students and researchers.
A Ph.D. degree (or close to completion) in Systems and Control, Signal Processing , Machine Learning , Applied Mathematics, or related field is required. Ideal candidates must have a strong background in Systems Theory, Automatic Control, Optimization and Statistical Learning. Excellent interpersonal, written, and oral communication skills and ability to write peer reviewed papers. An candidate must have the ability to collaborate with a multidisciplinary team of scientists and industry. Experience of developing efficient computer code is required.