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Collaborative Research Grant, in collaboration with the University of Gothenburg and Chalmers University of Technology. Start date is (expected to be) 1 January 2027 or as soon as possible thereafter. The project
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) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate
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acquire. Access to career guidance will be provided throughout the doctoral education. Research topic The position is focusing on the use of digital technology in child welfare services. Norwegian child
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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projects using international cohorts and registries. Analysing data on reproductive outcomes, hormonal health, pregnancy and disease trajectories. Collaborating with international experts, clinicians
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learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute to advancing efficient AI systems. Are you motivated to take a step
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manufacturing processes as well as energy storage. This is a diverse team that values collaboration, scientific excellence and research integrity. The primary role of the researcher is to develop new scalable
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, and the use of discarding low-quality entanglement (cut-off time). We will use simulation and analytical calculation for doing so. As part of the Doctoral Network collaboration his position includes
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from machine learning methods to more traditional statistical and econometric techniques. We are driven by science with purpose, pushing the academic frontier by publishing at the highest level in
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and lifetime validation. This position is part of a twin-PhD collaboration between Forschungszentrum Jülich (FZJ) and the French Alternative Energies and Atomic Energy Commission (CEA). The two projects