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and tomographic radar capabilities. Our team is responsible for the algorithms which derive the biomass data product. The post-doc project is about extending the biomass algorithm to also include data
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring of individual cows. The work includes
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solutions based on conceptual theory and empirical eco-evolutionary, molecular, and genetic data that can meet the needs of current and evolving plant production systems. For more information about the
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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Science is part of the Faculty of Health and Life Sciences. The research activities within this multidisciplinary department include the areas of aquatic ecology, cell and organism biology, evolutionary
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evaluating efficient and scalable techniques for systems that process and answer such queries (e.g., query optimization algorithms, adaptive query processing approaches). Conducting this research work includes
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. This involves formulation, implementation, and validation of novel hybrid models. The study emphasizes methodological innovation, scalable algorithms, and translation to industrially relevant multiphase reactors
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sequences will be systematically searched against established public databases to assess novelty, relatedness, and evolutionary origin. The postdoctoral bioinformatician will play a central role in viral
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the lab and bioinformatic/statistical analyses of next-generation sequence data. The project will be conducted in collaboration with Prof. Göran Arnqvist (Evolutionary Biology Centre, Uppsala University