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Field
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the following: Ensure you have all necessary documents available when starting the application process. You can review the additional job description section on postings for documents that may be required. Prior
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detection, inversion or imaging, system identification, and data fusion. Integrate DAS with complementary sensing modalities and computational tools, including AI or ML where appropriate, to improve
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Job id: 159026. Salary: £45,031 - £52,514 per annum inclusive of London Weighting Allowance. Posted: 21 September 2026. Closing date: 18 October 2026. Business unit: Faculty of Life Sciences
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Assistant Professor/Associate Professor/Senior Associate Professor/ Professor in Applied Mathematics
to those with research experience in fields with strong potential for external research grants and/or local industrial applications, such as mathematical/computational biology, inverse problems, and
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of novel approaches for contactless and multimodal vital sign monitoring and the interpretation of medical measurement data Contribution to solving inverse problems and reconstructing physiological states
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modeling and optimization for metasurface design, integrated with other optical components. Inverse design of optical components such as metasurface photonic-crystal unit cells or VCSELs. Collaborate
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to welcome an outstanding candidate to work in the field of ionospheric empirical modelling, D-Region ionospheric remote sensing, inverse methods, or ionospheric radio propagation. Background SERENE is a
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22 Aug 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Geosciences » Geology Researcher Profile Recognised Researcher (R2) Application Deadline 30
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. Demonstrated expertise in remote sensing, image processing, inverse problems, and machine learning. Proven experience in the analysis and interpretation of a wide range of geospatial and geodetic datasets
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machine learning challenges: how to learn from limited and heterogeneous data, how to combine data with physics-based models, how to solve inverse problems under uncertainty, and how to build models