51 parallel-computing-numerical-methods "Prof" Postdoctoral positions at Nature Careers in Denmark
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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Do you have a computational background and interest in developing novel tools for integrating structural biology data to understand regulation of disordered regions of proteins? Then the Viennet lab
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DTU Physics seeks a talented and motivated postdoctoral researcher for a 2-year position in computational nanophotonics. The goal is to use inverse design, such as topology optimization
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Postdoctoral researcher in Environmental Analytical Chemistry, with a focus on non-target screeni...
work of the research group, it will include elements of method development within sample preparation, instrumental and data analysis, quality assurance/control and semi-quantification, in the context
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; Collaborating closely with the ENGREENIT’s PhD candidate (starting 12 months later than the PostDoc researcher), supervised by the Assoc. Prof. Emil Draževic, and will jointly develop the heterogeneous
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Models”, led by Prof. Jonathan Brewer (Department of Biochemistry and Molecular Biology, SDU), Dr. Mike Barnkob and Prof. Torben Barington (Department of Clinical Immunology, Odense University Hospital
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postdoctoral position in methods development and analysis of multi-omics and clinical data for inflammatory skin diseases. The start date is October 1st, 2025, or as soon as possible thereafter. Research context
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Applications are invited for a post-doctoral position to develop and apply advanced mass spectrometry and data analysis methods to investigate plant-microbe interactions in soil, within the research
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(e.g. scikit-learn, PyTorch). Flexibility to adapt to different application fields. Independence and time management. Who we are The Computational Physics and Machine Learning Lab led by prof. Lucantonio