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research area. Prior experience working with Neural Radiance Fields or Gaussian Splatting. Prospective applicants should have a strong academic record with a solid background in Machine Learning (Deep
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science, human technology interaction, and machine learning. You will be working at the Human Media Interaction group in which computer science meets social science to investigate, design, and evaluate
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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differentiable programming applications (e.g., experimental design, machine learning for science). It will do so by bringing together a diverse team of PhD candidates with a primary focus in three different areas
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a small team of software developers working on AI prototypes and infrastructure. There is no teaching load in this position. Would you like to learn more about what it’s like to pursue a PhD at
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strong academic record with a solid background in Machine Learning (Deep Learning, generative models, diffusion models). Knowledge in sensor data processing and radaris a plus. Good programming skills
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Job related to staff position within a Research Infrastructure? No Offer Description How can we design new Human-AI interactions that enhance collaboration between people? This fully funded PhD explores
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Website https://www.academictransfer.com/en/jobs/350125/phd-candidate-in-causal-learnin… Requirements Specific Requirements You are/ You have: a MSc degree in statistics, machine learning, data science or a
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will have the chance to use cutting-edge techniques, including super-resolution microscopy (PALM, Smdm), single-particle tracking, machine learning, and latest state-of-the-art biochemical analysis tools