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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
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this project, we will develop new algorithms and computational schemes as well as further develop existing computational frameworks in the team. We will focus on two related frameworks in the project