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Field
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal network on climate action, particularly through the Climate Action Programme and its Climate Governance
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possess strong programming skills in Python and C++, with experience in PyTorch and pybind11 for developing and optimizing deep learning models. Familiarity with Sentinel-2 satellite imagery and experience
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
DNA thermodynamic database, coarse-grained simulations of DNA motifs, and existing experimental data to establish an AI model that is able to guide the construction of desired secondary structures
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use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and genes underlying responses to altered temperatures and parasite (co-)infection. We
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knowledge gaps. The project involves both linear and nonlinear dynamics modelling and analysis, as well as experimental testing. An equivalent test structure will first be constructed in the vibration
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this field. We are currently looking for researchers with strong energy systems knowledge, electricity, heat, and gas system components and flexibility modelling & simulation, and research software development
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cell walls, which have been implied in responses to the two parasites. We will also use the model species thale cress (Arabidopsis thaliana) as a resource to help identify the molecular mechanisms and
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elements/agents, adjusting expectations, mechanisms of contagion dynamics, feedback loops, and non-linear tipping of system dynamics. The SPHINX research program aims to fundamentally advance simulation
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materials/ Latent thermal energy storage is an advantage. Hands-on experience with experimental setups and measurement techniques/ sensors. Experience with system modelling and simulation (e.g., TRNSYS
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simulations can support data augmentation. Key deliverables of this project will be a multi-target radar tracker that learns to utilize radar targets’ amplitude signatures for data association and state