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deep learning workflows for tree species mapping. The position contributes to building a scalable system for forest monitoring by refining model performance and ensuring high quality geospatial data
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) Unsupervised machine learning and deep learning methods Analysis, visualization, and interpretation of learned design spaces Contributing to research outputs (prototypes, publications, open-source code) Profile
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needs. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with
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across multiple temporal scales, from short-range to up to 10 days ahead, using recent advances in deep learning. A central component of this effort is Anemoi , a framework developed by the European Centre
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and evaluating machine and deep learning models for spatio-temporal data. Positive attitude towards interdisciplinary collaboration, and willingness to frequently interact with the other team members
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the power of both classical and quantum computing resources? How can we exploit or take inspiration from quantum physics to develop cutting-edge machine learning? Your work will encompass a diverse array of
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, Computer Vision, Control Systems, Deep Learning, Digital Humans, Earth Observation, Educational Technology, Efficient AI, Explainable AI, Graph Representation Learning, Haptics, Human-Computer Interaction
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, with deep experience in appearance reconstruction and material modeling. Required Qualifications MSc in Computer Science or related field Strong background in computer vision and/or computer graphics
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online calibration framework that leverages deep learning to intelligently fuse data from multiple sensors (IMU, cameras, eye-trackers) By training models on a high-fidelity digital twin and exploring
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. Implement high-performance C++ and Python modules for simulation, rendering, and data processing. Explore and integrate deep learning techniques into graphics and simulation pipelines (e.g., PyTorch, JAX