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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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upcycling of alkaline solid wastes, with a special focus on designing and developing new strategies for high value uses of the final products. Expected start date and duration of employment The position is
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September 1st, 2025, or as soon as possible. The positions are for two years with potential for extension. We seek outstanding candidates to develop generative-AI-based statistical methods for blood-based
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performance in refurbished metal parts and contribute to assessing their environmental impact, including data input for CO₂e analysis tools. Expected start date and duration of employment This is a 1.5–year
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understand how materials interact with soft and hard tissue. Willingness to learn and integrate new methods or technologies, such as bioinformatics and 3D imaging processing. Ability to work both independently
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integration. The candidate will further study the role of critical topographical elements such as the Dogger Bank and the Elbe Palaeo Valley and how such may have influenced the landscape evolution and in turn
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and a demonstrable interest and experience in the use of computational methods. The candidate should combine the use of quantitative and statistical approaches with (solid) historical analysis, in line
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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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single-cell sequencing, genetic perturbation, and advanced spatial methods (including smFISH, light-sheet imaging, and whole-brain clearing). This project builds on our expertise in gene regulatory
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such as data scarcity, cultural sensitivity, inclusivity, and the need for robust preference optimization methods that go beyond standard fine-tuning. Key research objectives include: Developing Efficient