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(geometric deep learning, transformer-based approaches, ...) with a focus on protein-ligand interaction dynamics in collaboration with wet-lab researchers? If so, this fully funded PhD position might be
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learning techniques and advanced numerical simulation modelling to quantitatively study the electrical characteristics of state-of-the-art perovskite solar cells. The efficiency and stability of perovskite
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on advanced AI methodologies. Incorporating physics-guided deep learning models that explicitly integrate the underlying MRI signal formation process to enhance reconstruction reliability and interpretability
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the accurate prediction of reaction enthalpies and activation free energies for all relevant intermediates. In this project, a deep learning and generative design toolchain will be developed resulting in an ML
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of Melbourne Physics department are pursuing a very fruitful collaboration around the exploitation of liquid xenon detectors to search for rare events in deep underground laboratories. In the context
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research project focused on leveraging deep learning and advanced image processing techniques to improve the current tools for biomonitoring of aquatic ecosystems. This position involves the development and
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of Melbourne Physics department are pursuing a very fruitful collaboration around the exploitation of liquid xenon detectors to search for rare events in deep underground laboratories. In the context
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in MATLAB, R is a plus); Experience with deep learning and machine learning frameworks (e.g., TensorFlow, PyTorch); Familiarity with computational modeling of neural processes; Background knowledge in
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an industrial track (2 years at IMT Atlantique + 12 months at Sony STC, Germany). 1.1. Domain and scientific/technical context Generative and creative systems based on Deep Learning have recently emerged under
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Your job Are you looking for a PhD position where you develop state-of-the-art machine learning methods for the life sciences (geometric deep learning, transformer-based approaches, ...) with a