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criteria: Proven track record of publishing high-quality scientific articles in peer-reviewed journals. Excellent command of written and spoken English. Proficiency with advanced statistical methods
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Debugging: Developing mechanisms for synchronized state tracking across multi-core/multithread RISC-V architectures, ensuring consistent register states, memory coherence, and excep-tion handling during
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, computer-aided drug design or a related field. Track record of scientific innovation, as demonstrated by scientific publications, patents, relevant presentations, or software code. Demonstrated experience in
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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to revolutionize the research field in 3D learning. Research topics include: - Neural Rendering: 3DGS, NeRF, etc. - Generative AI: Diffusion, LLMs, GANs, etc. - 3D Reconstruction - SLAM / Pose Tracking (SfM, MVS
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area (e.g. biochemistry, molecular biology, molecular neuroscience) and have a strong track record of accomplishment. Candidates with proven interests and/ or experience in biochemistry or organelle
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or cellular biology) and have a strong track record of accomplishment. Candidates with proven interests and/ or experience in molecular neuroscience and/ or organelle proteomics and/ or the use and application
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of the department. He/she should have a good research track record in Algebraic Geometry and participate actively in the activities of the ERC-Project. Employment conditions To qualify for the position, applicants
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D
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with such environments. We investigate machine learning approaches to infer semantic understanding of real-world scenes and the objects inside them from visual data, including images and depth/3D