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turbulence profiles provide additional data for training and testing. A specific aim is to further develop the methodology of modular compositional learning (MCL). Here, an aquatic ecosystem model is
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tools or functional genomic information or OMICS to improve genomic prediction models. The persons hired will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise
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We are seeking highly motivated and talented applicants for a 1-year postdoctoral position (with the possibility of 1-2 year extension) in the area of efficient foundation model inference. Join us
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complement the Department’s ongoing activities in landscape-scale modelling, with a specific focus on nature-based solutions in agricultural landscapes. The successful candidate will be integrated
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application of cumulative impact assessment model using food webs, which is intended to assess impact of multi-stressors on trophic and non-trophic interactions networks. The modelling will be primarily based
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
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ecological data collection. The positions focus on improving detection and classification performance of deep learning models applied to millions of images collected in European monitoring programs. Key
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systems and a vibrant interdisciplinary community at Aarhus University, including collaborators in biophysics, neurodegeneration, spectroscopy, and computational modeling. You would join a dynamic research
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that addresses these issues. The center brings together experts on climate impact research and process-based modelling of biogeochemistry, agronomy, biology and geography from Aarhus University and University