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engaging research environment. The postdocs will work on cultural alignment and preference optimization of large language models (LLMs) for mid to low-resourced languages. The overarching goal is to develop
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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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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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water quality. You will be embedded within the Ethio-Nature project, funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use
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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 nature-based solutions that enhance local
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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
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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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for improved daily model calibration. The postdoc will be part of a research environment focusing on the Green Transition of Crop Production. This will involve optimizing and exploiting the potential