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models using general-purpose foundational models. Use of deep learning libraries: PyTorch. Experience publishing scientific papers at prestigious conferences on machine learning and AI. Other criteria 30
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models using general-purpose foundational models. Use of deep learning libraries: PyTorch. Experience publishing scientific papers at prestigious conferences on machine learning and AI. Other criteria 20
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, Physics, Electrical Engineering, Mechanical Engineering, or related field Knowledgeable in machine learning, deep learning, python, PyTorch, and programming in general. Knowledgeable in transformers and
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prevalent in our daily lives, we need guarantees of correctness in sensitive areas. The CertAI project aims to advance in this direction by leveraging certified model counting to certify machine learning
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phase-field simulations. Experience in developing AI or machine learning models for physics-based scientific or engineering applications. Proficiency in Python, scientific computing and machine learning
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models to quantify infection dynamics and therapeutic response endpoints • Develop and apply machine learning and AI-based approaches for pattern recognition, predictive modeling, and target discovery
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mechanics, information theory, stochastic processes, population dynamics, and neural networks. Within this framework, machine learning systems and artificial generative models will also be considered, where
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Title: Modelling dynamics and reactivity with Machine Learning Interatomic Potentials Supervisor: Professor Fernanda Duarte Start date: 1st October 2027 Applications are invited for a fully funded
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industrial partners. Main activities: • Develop constitutive models for granular media using physics-informed Machine Learning — high responsibility • Implement a coupled learning / boundary value problem
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, entomology and machine learning. This position will contribute to One Health epidemiology research projects on the impacts of environmental change on vector-borne and zoonotic disease risks. This work will