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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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-worker with strong research interest in 3D-cell culture research, microbiota and molecular virology. Goal of the project is to use genetically programmed commensal lactobacilli as living therapeutic
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their glial niche. Genetically encoded, fluorescent metabolite sensors will be used to study the underlying metabolite dynamics. The work will also incorporate various molecular biology techniques, as
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: 1
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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the environment, including traffic conditions, travel time, and cost. The project will define the DRL components (states, actions, rewards, policies), select and implement suitable DRL algorithms, and integrate
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, C/C++ and/or Java, etc.; experience with the implementation of specialized transport modelling software, optimization algorithms and procedures; strong ability and desire to learn new programming
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other siderophores using native production, genetically modified organisms and cell-free processes for the production of siderophores with the aim of achieving the highest possible siderophore
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partners regarding the horticultural experi-ments (Osnabrück University of Applied Sciences and the Institute of Plant Genetics of the LUH) and the optical superimposition of imaging with a processing laser