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Field
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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design scalable numerical methods for quantum master equations, implement high-performance simulations, and help build open-source tools for large-scale spin-system modeling. By improving our ability
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process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating plant population size and/or change. Qualifications: Requirements
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improvements. Develop neuromorphic sensory systems for biomedical and other application domains. Model and simulate neuromorphic devices, circuits and systems. Investigate spike-based signal processing and
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inventorying forest biodiversity. Possible areas include: indicators of functional or taxonomic diversity species-specific or habitat-based monitoring combinations of field data, remote sensing, and modelling
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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
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information, for example data derived from remote sensing, use point process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating
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in this project. As successful candidate, you will investigate predisposition of trees to drought stress by long-term fertilization, both experimentally and with modelling. You will analyze long-term
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applied statistical models, and will be part of a growing conservation technology hub at the department. The Department of Wildlife, Fish, and Environmental Studies offers a creative, stimulating, and
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of the future. The position involves doctoral studies where you will analyze genomic data, develop new models and simulations, and present new scientific results. You will also take courses at the doctoral level