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Field
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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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spectroscopic and imaging methods for the contactless detection of plant conditions. The position is being advertised as part of the joint project ‘LaserRoots: A new way for the sustainable production of climate
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adults, significantly impacting quality of life, and remain challenging to treat. Our research group has pioneered innovative methods to study voiding behavior in mice and developed live imaging techniques
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, understand its role in therapy resistance, and identify novel regulators using innovative CRISPR screening approaches combined with advanced imaging, single-cell transcriptomics, and phospho-proteomics.
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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into goal-directed behavior. We use state-of-the-art approaches including functional brain imaging, automated behavioral analysis, and computational neuroanatomy. We value a collaborative atmosphere, early
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initiatives at SWIMS. The ideal candidate will have expertise in IoT, sensor development, ecological monitoring, and advanced ecosystem imaging methods such as photogrammetry. The officer will collaborate
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the genetic factors influencing changes in brain structures, using brain imaging, computational and statistical methods of network science. Project Aim: The aim of the project is to uncover the complex
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laboratory and imaging facilities Access to a full range of equipment and laboratory facilities Inclusive and supportive lab culture with flexible working practices Applications welcome from candidates from
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awareness capabilities. The PhD will involve both simulation and experimental work. This includes designing and testing optical instrumentation and conducting observation campaigns to image and track