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Field
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the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning to enable innovative interactions with unmanned aerial vehicles
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-situ and operando conditions to mimic natural processes and enable practical applications. You will be part of a dynamic research group with extensive experience in nanoparticle synthesis and
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understanding and innovative fabrication processes to solve urgent problems in organic electronic devices, and enable new components with sustainable functionalities. Collaboration with industry partners will
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inherently spatio-temporal, i.e. physical processes around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from
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fuel structure, processing of different remote sensing and mapped data, statistical modelling, mapping of fire risk and analyzing the effect of forest management for fire behavior. Considered remote
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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. You must demonstrate strong mathematical maturity and also possess good programming skills. Furthermore it is important that you have good ability to communicate in English, both orally and in writing
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to automate the process of species classification. However, there are still several methodologies that need to be developed to integrate these models into a functioning workflow for ecologists. In this 4-year
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in a stepwise manner by the application of simple rules which are discrete in nature. This project under the leadership of Professor Frank Drewes aims to integrate neural methods of computation, and
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Specific entry requirements to be admitted to Energy and Environmental Engineering are met if a person has a degree (or equivalent studies) at advanced level in an engineering or natural sciences area