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in bioacoustic technology, such as passive acoustic monitoring (PAM), now enable efficient study of vocalizing species like birds and bats. Combining PAM with occupancy modeling allows for large-scale
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directions include: Quantitative genetics and phylogenetics: incorporating developmental constraints into evolutionary models and exploring how they shape patterns of variation. Modeling development from data
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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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and responsible development of the maritime industry. We address the multiple pressures that ships pose on the ocean and try to bridge the gap towards environmental management and marine spatial
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to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how these forces affect the degradation and lifetime of the machines. About the position The position is based
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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production and environmental considerations and facilitate driving on forest land in extremely dry or wet conditions. We will develop different tools. First, we will model soil moisture in the upper soil layer
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-planning of timber production and nature conservation, two important objectives in forestry. The work involves developing knowledge and tools for habitat modelling through 1) mapping existing habitats, 2
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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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address outstanding questions on behavioural evolution in canids. Your work assignments Understanding how behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model