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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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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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they can contain traces of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose
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Biochemistry advances multiphase flow and separation science to accelerate industrial innovation and implementation. About the research project The project aims to develop hybrid quantum–classical approaches
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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
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of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with
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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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Do you want to contribute to top quality medical research? Interested in developing tools that bridge computational science and nucleic acid technology? Whether your passion lies in computation
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification