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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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. Biomolecular condensates have emerged as a new paradigm to understand biological functions in living cells. Dresden has pioneered research in the field of biomolecular condensates and developed into its vibrant
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disciplines FELASA certification Experience with or highly interested in experimental immunology (e.g. in vivo models, flow cytometry, imaging) Motivation to learn and apply computational biology/bioinformatics
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knowledge or hands-on experience with Python, ImageJ, and/or MATLAB is highly beneficial for image processing and data analysis. Communication: Excellent written and spoken communication skills in English are
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operation code at the Funds Platform (Balcão dos Fundos) NORTE2030-FEDER-01794400, co-financed by the European Union through the NORTE 2030 Regional Program, of Portugal 2030, under the following conditions
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experiments, as well as for its applications in medical imaging. For that, it collaborates with MedAustron, an ion cancer therapy and research center. Within this collaboration, we design and develop ultra-fast
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positively valued: programming languages (Python, JavaScript), data analysis software techniques and tools, machine/deep learning (Pandas, SHAP , TensorFlow, etc.) and specific to image analysis, statistics
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. Advertising images can also link products to ideas of success. Thus, these forms of communication are not merely tools for conveying messages, but powerful agents that sculpt our society, influence our
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for an ambitious PhD student on perceptual foundation models. Your research is part of the Video & Image Sense Lab. Join our team! Recent breakthroughs in Artificial Intelligence resulted in the emergence
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amphibians and other aquatic vertebrates. Using advanced imaging, neurophysiological, and molecular biological methods, we investigate the olfactory system. Furthermore, we use the olfactory network as a model