Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
systems, from sustainable energy grids to autonomous mobility. In this ERC-funded PhD project, you develop cutting-edge game-theoretic control and optimization methods. Job description Modern society
-
-efficient computing? Do you want to contribute to cutting-edge research at the intersection of systems and control theory, optimization, circuit theory, and neuromorphic computing? The University of Groningen
-
the University of Groningen. The research will be carried out in the Systems, Control and Optimization Group. Our group is part of the Jan C. Willems Center for Systems and Control that unites the different
-
Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
-
part of a new lab at the Delft Center for Systems and Control, supervised by Gabriel de Albuquerque Gleizer. The research will allow you to gain deep insights across nonlinear dynamics, optimization, and
-
operational control strategies for circular CEA systems. Together, the two projects will establish the modelling and optimization foundations for future circular greenhouse and vertical farming systems. Your
-
. The state-of-the-art approaches in optimal climate control of greenhouses are based on implementing economic objective functions exploiting a time scale decomposition between short-term climate control/energy
-
with a strong background in applied mathematics, control theory and/or optimization to apply for a fully funded 4-year PhD position in the Smart Manufacturing Systems (SMS) group at the Engineering and
-
selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing
-
to increase resource use efficiency. However, managing and optimizing these new cultivation systems requires crop models that can predict plant growth, crop yield and resource use under highly dynamic