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, this language can also be perceived by parasitic nematodes. You will uncover how this language is created from plant side through transcriptome and metabolomics data analysis and biochemical characterization
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this language. You have an affinity for working with naturalistic stimuli and computational tools for motion/speech analysis (e.g. OpenPose, OpenFace). You preferably have experience with rapid invisible
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, data driven, computational and intelligent systems, all with a strong interactive component. The INtelligent Data Engineering Lab (INDElab) investigates intelligent systems that support people in
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of larval zebrafish. Data analysis: analyze whole‑brain calcium imaging recordings. Spatially resolved molecular methods: apply expansion microscopy (ExM) adapted for spatial transcriptomics to identify
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. The project will have a strong quantitative component relying on computer science, machine learning, policy evaluation and causal inference. In addition, it will also contain a strong business component, in
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tagging algorithm development as well as physics data analysis, with a focus on Higgs boson physics, top quark physics, and searches for new physics signatures. This is what you will do After the discovery
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the development of clinically relevant neuroprotective and recovery-oriented strategies. Overall, SYNERGY bridges experimental neurobiology, biophysics, and computational analysis to provide mechanistic insight
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
for rapid and resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding
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coordination failures reshape decarbonization pathways. Your research will combine methods from network analysis and agent-based modelling of economic systems to trace how international material and financial
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
resilient decarbonization. Your research will combine time-series analysis, supervised and unsupervised learning, and explainable AI methods to uncover the dynamic patterns preceding technological