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approaches, the application of meta learning, and the integration of convex optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms
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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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: 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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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic
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machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux, Windows) skills
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a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal
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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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. The programme is open to applicants from developing or transition countries in Africa, Asia, the Middle East or Latin America. Target Group post-graduates with excellent academic merit and potential who already
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Your Job: Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure
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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