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to the development of techno-economic models, and supervise the PhD candidate in all research activities related to energy economics. The researcher will work closely with the principal investigator and the project
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Ferrer (https://sites.google.com/site/eferrerdg/ ) at the school of aeronautics ETSIAE-UPM in Madrid, Spain. The researcher will develop/implement aero/hydro-acoustics functionalities in our existing high
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: Development of flexible exoskeletons for patient mobilization, within the framework of project 0361_EXOCARE_GNP_1_E, co-financed by the European Regional Development Fund ERDF within the framework
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. Comprehensive characterization of the new developed nanomaterials Where to apply Website https://sede.uvigo.gal/public/catalog-detail/27660208 Requirements Research FieldChemistry » Inorganic chemistryEducation
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teaching activities for professional development (not obligatory, though). Eligibility criteria Adequacy of the currículum: 50 Motivation letter: 20 Experience in the field: 30 Selection process An interview
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related to staff position within a Research Infrastructure? No Offer Description The PhD candidate will join a translational research project focused on the design, development, and preclinical validation
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-invasive and fast techniques to collect in operando data for battery cell characterisation and testing. Second, the candidate will develop Artificial Intelligence methods that can leverage these data
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Description The main tasks to be carried out by the selected candidate will be the following: ● Develop new methods for the uncertainty quantification of non-linear statistical models using the 'online
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INSTITUT CATALÀ DE NANOCIENCIA I NANOTECNOLOGIA | Universitat Autonoma De Barcelona, Cataluna | Spain | 2 months ago
ICN2 PhD Programme Programme description: The ICN2 offers a competitive PhD Programme to talented students who wish to develop a research career at one of the top European institutions in the field
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning