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Job Description We are seeking a motivated postdoc to work on the design and development of i) a variety of sample preparation methods (mainly for blood samples) and ii) new fiber-based substrates
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of TSN-based in-vehicle networks. These networks carry mixed-criticality traffic and use TSN with multiple traffic shapers and redundant communication. You will investigate methods for runtime analysis
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. Qualifications: You should have (or be close to achieving) a PhD degree. Background within computational methods for inverse problems, ideally tomography. Experience with development of numerical implementations
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independently Excellent English and basic knowledge of the Danish language As a formal qualification, you must hold a PhD degree (or equivalent) in environmental engineering or a similar scientific area. We offer
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background in feed processing technologies, biochemical, and chemical evaluation methods. Proven experience in experimental design, data analysis, scientific communication and writing. Demonstrated ability
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background in feed processing technologies, biochemical, and chemical evaluation methods. Proven experience in experimental design, data analysis, scientific communication and writing. Demonstrated ability
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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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breakthroughs in the study of transport across interfaces by utilizing twisted membranes. This innovative method offers an exciting way to tune the properties of the materials. The goal of the Postdoctoral
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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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solving You are highly experienced in a research environment with a well-established publication track record You have formal training in mathematical, statistical, and machine-learning-based analysis