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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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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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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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these new methods at different sites and time periods, compare them with established approaches, and finally demonstrate their potential in a Europe-wide ecosystem reanalysis. The outcomes will include open
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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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environmentally friendly chemical production. Due to the cross-industry and cross-disciplinary nature of the topic, experts from various disciplines work closely together on this. Modern biotechnological methods
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method implemented into the EURAD-IM. In atmospheric chemistry modeling, the 4D-var method is a powerful tool to assess the state of the atmosphere and the corresponding emissions that are in compliance
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image reconstruction methods in a cell microscopy setting using image sequences as well as focus stacks Investigate instance and panoptic segmentation for endosymbionts and track them over time Implement
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and