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: machine learning or deep learning (e.g. PyTorch) scientific data pipelines or large datasets knowledge graphs or structured data systems GPU or distributed computing scientific machine learning or physics
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scientific curiosity. You thrive at the boundary of robot learning, computer vision, deep learning, and simulation, and you are excited to see your research running on real robots. You communicate clearly
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methods so that design decisions can be understood, validated, and trusted. As a postdoc, you will: Develop generative AI models (e.g., variational autoencoders, diffusion models, or reinforcement learning
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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the dispersion of these macroplastic items in these flow fields; comparing the results of the simulations to results from an experimental campaign of floating trackers; collaborating with a postdoc and two PhD
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languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also