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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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of Large Language Models Time-Series Data Prediction and Modeling Intelligent Decision-Making and Optimization Algorithms Strong programming skills (proficient in Python, PyTorch/TensorFlow, etc.). Strong
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and Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group
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. The models will be used to predict dynamic responses to stressors, sleep disruptions, and diagnostic tests, as well as the long-term changes that occur during disease. A key challenge of your work will be
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular
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(iii) the integration of enzymatic ex vivo models with advanced constitutive and damage laws. In the longer term, this work will contribute to a predictive framework of menopausal tissue fragility, as a
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on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models) to assess and interrogate the issue of
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modelling. MISSION You will actively contribute to the development and evaluation of new hybrid computational method to predict biological tissue deformation with subject-specific material properties