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or Functional ultrasound imaging or Electrophysiology (neuropixels) in behaving animals Quantitative data analysis and computational modeling of network activity Data acquisition systems, signal processing and
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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interdisciplinary, and together we contribute to science and society. Your role The Junior Research Group in AI in Biomedical Imaging conducts applied AI research focused on biomedical image computing. Our work
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information encoding and computation in neocortical circuits. The successful candidate will lead an ambitious project on synaptic and circuit mechanisms of sensory processing in primary visual cortex (V1
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) milling. This programme will involve state-of-the-art imaging approaches to visualise molecular details of the intricate relationships between chromatin and nuclear condensates. The successful candidate
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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of PhD submission. Strong expertise in advanced confocal and/or light sheet imaging. Strong expertise in quantitative image analysis approaches. Excellent molecular biology skills. Strong interest and
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outputs, including: A return-of-data blueprint Prototype participant reports An evaluated framework for future implementation Your Profile Master's degree and PhD in computational biology, bioinformatics
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. Experience in working with animal models, cell isolations from tissues (lung, intestine), human tissue samples, multi-parameter flow cytometry, molecular biology and fluorescence imaging will be preferred. We