Sort by
Refine Your Search
- 
                
                
                
aspects of the project, including analyzing near-Sun observations of CMEs and shocks, running three-dimensional numerical simulations to model the Sun-to-planet evolution of CMEs and their sheath regions
 - 
                
                
                
on hierarchical Bayesian models, that allow us to integrate heterogeneous, but complementary, ecological and environmental data. The doctoral researchers will focus on developing statistical models and methods
 - 
                
                
                
, and running and interpreting one- to three-dimensional solar wind simulations to define new inner boundary conditions for global heliospheric models. Requirements: MSc (or equivalent) in space physics
 - 
                
                
                
the "Mercury in the solar wind" ERC project at the Finnish Meteorological Institute. The PhD student will apply our global particle-based models to study the solar wind influence on Mercury and its environment
 - 
                
                
                
stability theory, modeling & identification, optimal control, certifiably safe & robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven
 - 
                
                
                
-on experience with (pressurized) chemical reactors Experience with modelling and simulation software (e.g. Aspen) Good organization and data management skills We expect the candidates to be able to start
 - 
                
                
                
to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
 - 
                
                
                
into an expert in magnetic components in modern power electronic systems. The following topic with international secondments is offered: Design of inductors using time-domain loss models Objective: Design and
 - 
                
                
                
development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model
 - 
                
                
                
) outline of research skills, and iii) how these skills could support this research Copy of M.Sc. diploma (or equivalent) AND a transcript of completed credits. The requested documents must be compiled in