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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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the implementation and testing of algorithms. * Strong programming skills in R or Python. * Familiarity with data science and visualization libraries in R or Python. * Experience with GitHub, conda environment and
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, humanities and social sciences; Business and economics; Education; Engineering; Environment; Health; Information technology and computer science; Law; Music, visual and performing arts; Science Need help
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 3 days ago
breeding populations to investigate the genetic basis of traits such as visual anther extrusion, heading date, plant height, and grain yield. You process genotypic data, ensure its alignment with phenotypic
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. sound installations, visual storytelling, participatory mapping, or community-engaged exhibitions. We particularly welcome applicants who are open to combining academic analysis with creative modes
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, particularly alkaline etching and anodising, to understand the processes that determine the visual appearance and optical properties of aluminium surfaces. The primary goal is to uncover the factors
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for culture and creative domains. These domains may include sound, music, visual art, 3D graphics, movement, or multimodal combinations thereof. The research will employ a mixed-methods approach: the search
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voiding behavior in mice and developed live imaging techniques to visualize sensory signaling in the bladder wall and dorsal root ganglion (DRG) neurons. In addition, we explore these pathways in human
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to visualize sensory signaling in the bladder wall and dorsal root ganglion (DRG) neurons. In addition, we explore these pathways in human bladder tissue samples. The LICR lab is looking to recruit 1 highly
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spatial planning. Translate scientific results, in collaboration with stakeholders, into clear, engaging visualizations and communication materials that support understanding and inform evidence-based