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to correct or account for these biases, and build predictive models that simulate biological responses to in silico perturbations such as genetic or pharmacological interventions. The project aims to advance
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to correct or account for these biases, and build predictive models that simulate biological responses to in silico perturbations such as genetic or pharmacological interventions. The project aims to advance
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-funded DECIPHER-M consortium (9 partners, €9M), we are building multimodal foundation models that integrate imaging, text, and structured clinical data to predict metastasis risk and identify tumor origin
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large datasets in wheat, Develop and implement novel approaches for genome-wide predictions of complex traits. Your qualifications and skills: You hold a MSc in plant science, plant breeding, biology, or
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and
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, predict, and manage them remains fragmented across disciplines. The Understanding and Predicting Impacts of Climate Extremes under Global Change Doctoral Network (CLIMES DN) (https://www.climes.se/climesdn
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modelling is a valuable tool to revealing the source of UTLS aerosols, the origin of water masses, and formation processes of cirrus particles. Your key responsibilities include: Preparation, operation, and
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the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration
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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in