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, The chair of Geoinformatics, and The chair of Algorithmic Machine Learning & Explainable AI) and access to external partners and datasets. Your tasks will include: • Build a comprehensive multi-modal urban
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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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Research Project“ Transforming Cardiac Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts
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positively valued: programming languages (Python, JavaScript), data analysis software techniques and tools, machine/deep learning (Pandas, SHAP , TensorFlow, etc.) and specific to image analysis, statistics
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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tackles fundamental challenges in multimodal representation learning by developing novel approaches to align distinct embedding spaces from speech and sign language modalities. Sign languages encode
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Research: Visual Exploration and AI Prediction Modeling of Real-Life, Multi-Modal Data” as a PhD-Position in machine learning. You will work alongside leading experts at the Computational Imaging Research
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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-molecule techniques. Coding skills for data analysis, pattern recognition, machine learning, kinetic modeling, etc. Advanced optics, biochemical wetlab, and/or bioengineering experience. Required: MSc degree
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, or willingness to work with them Experience with multi-modal machine learning methods Familiarity with formal linguistics, particularly formal semantics and pragmatics We encourage applications from individuals