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for Surface enhanced Raman Scattering (SERS). You will spearhead and coordinate our development and optimization of new SERS substrates as well as sample preparation of clinical samples that we receive from our
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part of the Dynamical Systems Section. We perform research within a broad range of areas within dynamical systems including modeling, optimization, forecasting, and controlling in both deterministic and
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have successfully defended a PhD thesis in a relevant discipline (computational mechanics, mechanical/aerospace engineering, simulation technology, applied mathematics, etc.). If you have not received
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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time
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computational humanities audiences. Qualifications Applicants should: Hold a PhD or equivalent qualifications in computational history, computational archaeology, computational social science, cultural evolution
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tasks will consist of: Brainstorming and designing how to handle samples for multiple analysis. Optimization of protocols for tissue preparation before further analysis of disease mechanisms (and drug
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD