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international network structure in order to integrate existing competences and knowledge, and to link various actors within the complex area of climate change. The PhD position is also supervised by Prof. Dr
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professional network through international research stays and develop innovative solutions that support the global transition to sustainable energy systems. Apply now to make a difference in the field
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networks involved in CHC perception, particularly in the context of prezygotic reproductive isolation within a species complex of parasitoid wasps (Nasonia). Our previous research has already deciphered
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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diseases. This project will help to make a substantial difference towards automated drug discovery and helping to reduce suffering worldwide. The research will be conducted using state-of-the-art equipment
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. The Studentship will include a bursary (€16,000) and EU fees. For non-EU applicants, a non-EU fee waiver may also be available, but this cannot be guaranteed (a difference of approximately €6000p.a.). Selection
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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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technologies. Agricultural applications present a unique opportunity for AI systems as they often involve repeatable tasks within a relatively low-safety-risk environment, unlike public or transportation
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning