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Description The majority of hydrological models rely heavily on the principle of mass balance, often represented through Ordinary Differential Equations (ODEs). These models encapsulate
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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on the following tasks with either with a stronger model-development or application focus: Design knowledge-graph-augmented transformers and retrieval-augmented generation (RAG) pipelines that enable
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with attractive conditions A broad range of further education and professional development programmes (for example language courses) An occupational health management model with numerous attractive
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scientists on, e.g.: Developing self-supervised learning frameworks to extract features from unlabeled high-resolution microscopy data Training and evaluating segmentation models for detecting and
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Description Water can move in two interconnected realms: the fast, visible rivers at the surface and the slower, pressure-driven flow within substrates. Today, engineers can model each realm
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teacher-student synchronization. However, this synchronization necessitates an exact student model, making the ACT often inexact and susceptible to uncertainty and variability. The project will aim
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accurate flood hazard and risk estimations for present and future cli-mates. The improved model chain consisting of a non-stationary weather generator, a rainfall-runoff model, coupled 1D-2D hydrodynamic
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Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg | Magdeburg, Sachsen Anhalt | Germany | about 6 hours ago
to understand the reaction mechanisms and stir the selectivity towards hydrogenated N-forms, while suppressing the formation of undesirable products Detailed modelling, simulation and optimization
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
nano-structures. In this project, we will combine numerical models, experiments, and artificial intelligence (AI) to guide the design of specific DNA nanoconstructs. The primary goal is to build an AI