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Field
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of visualization and multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240
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, pharmacology, inflammation, cancer and neurobiology. We also teach students studying at various programs and independent courses in cell biology, physiology, neurobiology, anatomy and histology
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. Conducting most of the development in a digital environment is particularly important when dealing with mobile, heavy and powerful machines, and especially in the early development phases when they exist only
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and programming are highly meriting, especially in gene regulatory networks, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially CRISPR screening, is highly meriting
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. Conducting most of the development in a digital environment is particularly important when dealing with mobile, heavy and powerful machines, and especially in the early development phases when they exist only
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multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240 ECTS credits
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to assimilate knowledge at the research level. Understanding and experience in machine learning and computer vision. Knowledge, experience, and strong interest and in AI and XR development. Knowledge and
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, and manipulation of high-dimensional imaging and mass spectrometry data Experience in designing and maintaining reproducible and scalable analysis workflows Solid foundation in statistics and machine
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or Machine Learning for data analysis Knowledge of diversity and equal opportunity issues, with specific focus on gender equality Great emphasis will be placed on personal skills. Trade union representatives
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existing omics and machine learning-based pipelines to process and postprocess this data. The Project Assistant will be encouraged and given the opportunity to lead their own project analyzing proteomics