PhD student position in Joint Sensor Fusion and Machine Learning for Autonomous Systems at the Department of Electrical Engineering at Linköping University.
Methods for distributed learning of augmented state-space models will be researched. This class of models enables domain knowledge to be incorporated in the learning process to guarantee a minimum of performance and enable an efficient learning process; still, they are flexible enough to permit learning of partially unknown model dynamics and inputs. Thus, the targeted methods are foreseen to play an important role in realizing large-scale sensing systems. Focus of the research will be on learning of state-space models augmented with sparse Gaussian process models. To enable distributed and efficient learning of the models, techniques for distributed Gaussian process regression, such as Bayesian commit machines and inner product quantization, will be integrated with distributed filter algorithms, such as covariance intersection, consensus, and diffusion Kalman filters. The use of information-gain based active learning strategies to streamline the learning process, both on local and global scale, will be investigated.