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research on topics including, but not limited to: Continual learning, with a focus on novel optimization techniques and memory architectures. Advancing the reasoning capabilities of large language models
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Are you inspired by the quest for new materials for solar cells, spintronics, and quantum technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you
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mitigate turbulence under diverse urban layouts and meteorological conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement
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-analysis, regression modelling, machine learning). You have a solid basis in at least one common high-level programming language (e.g. R, Python). You enjoy collaborative research in international
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for model calibration and validation. Develop models - potentially adopting first principles theories, coarse computational description and/or machine learning / artificial intelligence methods – to describe
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, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion/transformers), multimodal representation learning, and experience in
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of the Swammerdam Institute for Life Sciences at the University of Amsterdam (UvA). BDA works on the development of methodology for data mining, machine learning/deep learning, data fusion, and modelling and
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(completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative modelling (diffusion
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learning, with a focus on novel optimization techniques and memory architectures. Advancing the reasoning capabilities of large language models (LLMs). Developing methods to improve the safety and robustness
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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