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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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classification and multi-layered environment mapping - Digital twin generation for natural environments - Semantic scene-understanding in natural environments for robust decision-making - Learning-based
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learning and deep learning models for trait prediction and climate-resilient wheat breeding. Analyze time-series UAV data using crop models in combination with genomic and agronomic information. Collaborate
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to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a
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, and may well end up leading, a direction of their own, perhaps one we have not yet imagined. Nature used DNA for essentially a single purpose over four billion years; we are learning to use it for
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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at the interface of automatic control, electrochemistry, and machine learning. The position will also involve close collaboration with another postdoctoral researcher working on a complementary project in physics
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. The following experience will strengthen your application: industrial product development or manufacturing research modelling and simulation, digital twins or digital threads AI, machine learning
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both a learning environment and a social arena. Against the backdrop of increasing polarization recent policy developments concerning school safety and crime prevention Schools Against Crime (SOU 2024:14
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required — we value biological curiosity and willingness to learn. Have good teaching abilities. Have awareness of diversity and equal opportunity issues, with specific focus on gender equality. Great