[Belgium] Postdoctoral researcher in advanced fluorescence microscopy at Universiteit Hasselt plus 1 more

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[Belgium] Postdoctoral researcher in advanced fluorescence microscopy at Universiteit Hasselt Posted: Postdoctoral researcher in advanced fluorescence microscopy at Universiteit Hasselt Overview Within a joint project with Prof. K. Braeckmans (UGhent) and Prof. J. Hofkens (KULeuven) properties of nanomedicines will be evaluated for delivering nucleic acids to the Retinal Pigment Epithelium (RPE), the outer lood-retinal barrier in the eye beneath the neural retina. To address this issue several advanced fluorescence microscopy methods will be extended and developed. The nanomedicine internal structure will be studied by the newest super-resolution microscopy techniques such as STORM and STED. The transport and stability of nanomedicines in vitreous gel, neural retina and inside living RPE cells will be studied by a newly developed correlation spectroscopy method and dual color 3-D SPT. The knowledge gained in this project from the various microscopy methods will enable a rational and efficient development of nanomedicines for nucleic acid therapy.

[UK] PhD studentship in natural language processing at University of Edinburgh Posted: PhD studentship in natural language processing at University of Edinburgh Overview PHD studentship in natural language processing Specialty Areas: Cognitive Science; Computational Linguistics; Psycholinguistics Natural Language Processing Institute for Language, Cognition and Computation School of Informatics University of Edinburgh The Institute for Language, Cognition and Computation (ILCC) at the University of Edinburgh invites applications for a three-year PhD Studentship starting in academic year 2011/12. ILCC is dedicated to The pursuit of basic and applied research on computational approaches to language, communication and cognition. The studentship is associated with the EPSRC project `An Integrated Model of Syntactic and Semantic Prediction in Human Language Processing.` The aim of this project is to develop a model that Combines probabilistic incremental parsing with compositional distributional semantics in order to achieve a broad-coverage account of human language processing that can simulate reading time data. The Project will also apply this model to NLP tasks such as text Simplification.

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