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translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model–graph neural network architectures for gene perturbation prediction, including the design and
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experience in the following areas: Applied Machine Learning for Autonomous Systems: Experience developing and deploying ML models for perception, prediction, or decision-making in autonomous driving or robotic
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of hybrid foundation model-graph neural network architectures for gene perturbation prediction, including the design and implementation of novel training strategies under experimental constraints, e.g
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outcomes, and multi-OMICS profiles, the project will generate predictive models to guide safer and more effective, individualized steroid use. As such, the candidate will be responsible for data
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and clinical research by embedding the “bed to bench to bedside” research approach. Fundamental research focuses on induced pluripotent stem cell (iPSC)-derived neuronal models to elucidate
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attention to scientific rigor and interpretability Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions Clear written and verbal
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approach. Fundamental research focuses on induced pluripotent stem cell (iPSC)-derived neuronal models to elucidate the molecular and cellular alterations contributing to neurodegeneration in familial and
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the department’s Environmental Sensing and Modelling (ENVISION) unit, the ‘Remote sensing and natural resources modelling’ group is carrying out impact-driven research, geared towards monitoring and predicting
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evaluating adaptive Medium Access Control (MAC) and network‑layer protocols to enhance performance in shared spectrum environments. Developing AI‑driven methods for real‑time interference prediction and