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and geometric deep learning, or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record, educational vision, and future research goals align with
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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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across both surface and subsurface layers. This includes constructing robust feature extraction pipelines, attention-based fusion architectures, and deep learning models that accurately characterize cracks
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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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cybersecurity expertise with modern AI techniques such as machine learning, deep learning, or large language models? Then we strongly encourage you to apply. You will join an established team with 25+ members
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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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and behavioural speech features. Integrate neuroimaging, speech and clinical data using multivariate and machine-learning approaches (e.g. UMAP). Investigate the effects of deep brain stimulation
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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
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machine-learning approaches (e.g. UMAP). Investigate the effects of deep brain stimulation on speech production in relation to individual connectivity profiles. Coordinate closely with clinical