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, you will explore how data-driven models capturing the state-of-health and degradation can be integrated in the battery model. You will develop these machine learning-based proxies together with a
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developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and applications of AI. Proficiency in Python, leading ML frameworks
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these machine learning-based proxies together with a postdoctoral researcher working in this project (see below), leveraging data from experiments in our project. Third, you will explore how local connection
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learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In this role, you will
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) assessment, such as neurophysiology, extended reality, eye-tracking, psychometrics, and machine learning. About the organisation At the Faculty of Behavioural, Management and Social Sciences (BMS), we unite
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modelling (diffusion/transformers), multimodal representation learning, and experience in computer graphics/animation. Strong programming skills, especially in Python. Ideally, you have experience with
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modelling (diffusion/transformers), multimodal representation learning, and experience in computer graphics/animation. Strong programming skills, especially in Python. Ideally, you have experience with
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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modelling (diffusion/transformers), multimodal representation learning, and experience in computer graphics/animation. Strong programming skills, especially in Python. Ideally, you have experience with
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: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation techniques Translating deep learning models into clinical