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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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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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development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en/department
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preferences, your work may focus on the theoretical foundations of queries and mappings in this context (e.g., formal results on fundamental properties of relevant languages) or on developing, implementing, and
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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
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research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent Surfaces, including both joint baseband processing and synchronization across
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conducted implementations of algorithms and simulations using contemporary GPU hardware, or Profound knowledge and experience of the DUNE software environment (https://dune-project.org/ ) (knowledge and
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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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Safe and efficient ice navigation supported by satellite data Join us at the Division of Geoscience and Remote Sensing and help advance knowledge about sea ice dynamics and develop the capability