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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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present. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python skills, preferably some
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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of Environmental Engineering, ETH Zürich and matriculate in ETH Zürich. The research is related to development of experimental and modeling techniques to identify emission sources, simulate the airborne transport
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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines
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Science, or related field Knowledge of flexible antennas, wireless communications, and machine learning Good skill set in signal processing and optimization techniques Proficiency in Python for modeling and machine
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in modelling, simulation, or data analysis of energy systems Knowledge of machine learning or artificial intelligence methods Programming experience (e.g., Python, MATLAB or similar tools) Experience
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse