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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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available in the further tabs (e.g. “Application requirements”). Objective The aim of this programme is to enable former scholarship holders of the “Development-Related Postgraduate Courses (EPOS)” to carry
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop
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Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding
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Your Job: Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets Integrate ontologies and metadata (e.g., tissue, cell
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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Your Job: We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to
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will be developed using the example of the novel Earth Explorer EarthCARE and will be integrated as observation operator to the sophisticated data assimilation system of the EURopean Air pollution