[Denmark] PhD Scholarship on Relativistic Electrons in Electric Discharges at Technical University of Denmark plus 1 more |
| Posted: PhD scholarship on RELATIVISTIC ELECTRONS IN ELECTRIC DISCHARGES is available at DTU Space, Copenhagen, with start date September 1st 2010, or as soon as possible thereafter. The position, which is funded by The Danish Council for Independent Research | Natural Sciences, is related to a mission planned for the International Space Station: `The Atmosphere-Space Interactions Monitor` (ASIM) for observations of extreme thunderstorms and their influence on the atmosphere. The project will study how electrons are accelerated to relativistic energies in electric discharges in the atmosphere. The question has been raised after the surprising observations from satellite of high-energy radiation from the atmosphere above powerful thunderstorms, and from lightning and laboratory discharges. The radiation is bremsstrahlung from electrons that are accelerated to unexpected high energies. The acceleration process is not well understood but is interesting because it influences the dynamics of the discharges and their effect on the atmosphere.
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| [Germany] PhD Position in Computer Vision and Robotics at Forschungszentrum Jlich GmbH Posted: D039/2010: Computer Vision and Robotics The Institute of Chemistry and Dynamics of the Geosphere - Phytosphere (ICG-3) investigates dynamic processes in plants and their interaction with dynamic environments. The Quantitative Image Processing Group at ICG-3 is seeking a PhD Student to begin work on a three-year PhD project. The group develops algorithms and methods for image-based plant measurements. The algorithms to be developed generally analyze multidimensional signals, in particular multichannel or multi-camera image sequences. This includes, for example, 3d reconstruction of deformable objects for measuring the surface expansion of plant leaves, stereoscopic robot vision, diffusion-like methods for noise reduction, image segmentation and reconstruction of diffusion tensor MRI data. Methods applied include suitable physical modeling of the acquired data, e.g. by PDEs, optimized discretization, variational and other energy minimization methods, Bayesian inference, energy optimization and channel representations.
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