10 bayesian-object-detection PhD positions at Swedish University of Agricultural Sciences in Sweden
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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Spectrometry This doctoral project aims to improve our understanding of chemical contaminants in food, focusing on detecting both known and previously unidentified compounds using suspect and non-target
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ecology, biodiversity monitoring, and molecular methods. The project explores how airborne environmental DNA (eDNA) can be used to detect and monitor migratory species, invasive species, and pathogens
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to manufacture novel filtration systems, using green approaches through four main missions: research, training, management and innovation. This objective will be achieved through a structured research training
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and predict the sensitivity and resilience of Swedish conifer forests to interacting soil fertility and droughts. Specifically, we want to detect whether long-term fertilization practices have modified
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economics is one. The PhD student will be placed in the research group “Agricultural and Food Economics”. In this group, the successful candidate will find a stimulating context with colleagues who focus
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Sweden’s national environmental objectives. Read more about the department: https://www.slu.se/ekologi Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work
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on the Ultuna campus in Uppsala. Here, you will find expertise in plant biology, mycology, plant pathology, microbiology, food science, computational genetics, chemistry, and biotechnology, as well as competitive
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are looking for a PhD student in biogeochemistry of forest soils with a focus on N2 fixation in ectomycorrhizal tubercles. The objectives of the PhD project are to quantify N2 fixation in ectomycorrhizal
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areas include: indicators of functional or taxonomic diversity species-specific or habitat-based monitoring combinations of field data, remote sensing, and modelling new techniques for detecting and