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PhD position in Experimental Physics with focus on photonics and materials science (applied aspects)
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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, you will: - conduct independent research combining field investigations and numerical modelling of multimodal data (taking into account subsurface structural complexity and anisotropy); - develop
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patients develop a durable response. Many researchers are investing efforts to understand the complexity of anti-cancer immunity and develop diagnostic approaches that accurately predict therapy benefit and
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international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Project description The project focuses on the design and analysis of modes
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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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transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition
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other training and networking opportunities throughout Sweden. You will be further affiliated with the Laboratory for Molecular Infection Medicine Sweden (MIMS; https://www.mims.umu.se/ ); MIMS is a
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta