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Field
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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating
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none of the sections scoring less than 5.0) TOEFL score of 550 or more (computer based test 213, internet based 79) Cambridge/Oxford - Advanced or Proficiency level Selection The selection among
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computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at bachelor’s and master’s levels, as
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analyses and machine learning. Some data for the project already exist, but additional data will be collected from behavioural tests on privately owned pet dogs in Sweden and abroad (Europe). Travel and time
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work with large-scale behavioural data sets using a range of approaches, including heritability analyses and machine learning. Some data for the project already exist, but additional data will be
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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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Meritorious will be considered experience in: - Microscopy-based imaging - Immunohistochemistry - Work with clonal cell lines - Work with other model organisms such as D. melanogaster or D. rerio You are a
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emphasis on Image Analysis and/or Geomechanics Fluency in spoken and written English Willingness to learn Swedish, as necessary for providing teaching support at undergraduate level Genuine interest in
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electronic doping to control and modify their electronic characteristics. The project’s goal is to develop fundamental understanding and innovative fabrication processes to solve urgent problems in organic
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the fields of cloud computing, computer networking and immersive systems to develop elastic and cost-efficient cloud-based AI pipeline to tackle climate change and support sustainability. Some of the tasks