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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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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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, 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
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work experience Deep interest in space, technology, innovation and entrepreneurship Experience with organizing events (e.g. Hackathons, StartupWeekends, etc) Excellent written & spoken English
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Job description section above). Theoretical and practical experience and passion in at least one of the following topics: Machine Learning: Deep Learning, Federated Learning (vertical, horizontal