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results and to make parameter estimation more efficient. The project will apply and evaluate these new methods at different sites and time periods, compare them with established approaches, and finally
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for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics-aware learning methods with domain decomposition techniques, enabling parallel training and
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Develop methods and workflows to construct robust co-regulation networks from large
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In
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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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a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences
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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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inequalities in care homes. Using participatory mixed-methods research, the project will work closely with care staff, residents, managers, and regional partners to explore lived experiences of digital care
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statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and related in space and time and to behavioral events. Core Tasks: Getting familiar with
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at international conferences and learn about state-of-the-art methods in machine learning, reinforcement learning and computer vision for the life sciences Your Profile: Excellent Master’s degree in engineering