Economic and financial activities are often influenced simultaneously by a decision and random factors. Since the decision parameter must be constructed mostly without knowledge of a random element realization, an optimization problem depending on a probability measure corresponds to this situation. Usually in applications this measure must be replaced by an empirical one.

Decision making (DM) is a targeted choice of actions based on given knowledge and preferences. Normatively, Bayesian DM, maximising expected utility, should be used under uncertainty but this happens less than desirable. Often, imperfection of the DM participant can be blamed as it limits the deliberation effort spent.

Digital image acquisition is often accompanied with its degradation by blur (out-of-focus, motion etc.) and noise. In many cases, the degradation process can be modeled by convolution g=u*h+n where g denotes the acquired image, u the original image, h the convolution mask (blur), and n random noise. The goal of deconvolution is to recover the original image based on the observed image.

Project deals with novel advanced methods for global analysis and control design for decomposed strongly nonlinear state space dynamical models with complex interactions of their components. Focus will be made on multivalued uncertain dynamical systems, especially the multivalued systems class that might be called as the "systems of systems".

The overall objective is to develop advanced packaging for power supply components and new generation memory systems having application on Electric and ICE propelled vehicles. One of the main challenges of the introduction of the electronic systems in the automotive, in particular for electrified vehicles, is the reliability and the availability of the power supply systems, which must assure the