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PhD Scholarship Develop multimodal machine learning models to predict glioblastoma treatment outcomes using imaging and clinical data. Work with real-world data from John Hunter Hospital in a
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for modelling protein structures and protein-ligand (metabolite) interactions. Using multi-scale molecular models, we aim to predict the structural features of gibberellin (GA) transporters, a key phytohormone
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–robot interaction and ergonomics Automation and robotised workplaces (industry use-cases) Field robotics and harsh-environment robotics Predictive maintenance, sensor systems, multimodal monitoring AI/ML
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–robot interaction and ergonomics Automation and robotised workplaces (industry use-cases) Field robotics and harsh-environment robotics Predictive maintenance, sensor systems, multimodal monitoring AI/ML
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pain, a critical and currently missing component in translational research. These new models are intended to enable accurate prediction of analgesic efficacy and disease-modifying effects of novel
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, and failure-mode prediction in distributed charging and energy-management environments as part of the BANNER research project Investigate fault-tolerance and fault-recovery mechanisms for decentralized
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on iPSC‑derived sensory neurons and reporter cell technologies, ultimately contributing to more predictive, human-relevant models for translational pain research. Important to note Are you interested in
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pipelines to predict, prioritize, and validate bioactive compounds. Your work will help accelerate the process from genomic data to lead molecules. Strong communication skills and the ability to collaborate
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therapeutics by protein design. This project will apply cutting-edge generative AI methods—including protein design, structure–function prediction, and multimodal learning—to develop and optimize a new
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://www.biologie.uni-hamburg.de/en/forschung/grk2530.html). The doctoral candidate will investigate the effects of increasing flooding frequency (as predicted under climate change) on the interactions between alluvial