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experience in data acquisition and analysis of advance microscopy imaging techniques (e.g. confocal microscopy, TIRF, FRET, STORM, DNA-PAINT) is desired. WE OFFER: Career development in a multidisciplinary and
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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developing computer-vision systems for faunistic data analysis Present project goals and achievements in national and international scientific conferences Supervise technicians and students in training data
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intelligence methods for data analysis, including image data processing present results through reports and presentations to scientific and clinical audiences perform heterogeneous data analysis in the context
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medical machine learning for a talented postdoctoral researcher (f/m/d) to deepen their expertise and interest in machine learning for medical image analysis and build their early scientific career. About
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phenotyping, including image analysis evaluations, for trait quantification Handle NGS datasets for RNAseq or SNP detection and linkage analysis using R Your qualifications and skills: You have a PhD or
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improved patient outcomes Integration of findings into translational research, collaborating closely with clinicians, imaging specialists, and bioinformaticians to optimize interventional oncology treatments
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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Proficiency in bioinformatics, single-cell omics, or advanced imaging analysis Familiarity with functional readouts (e.g., MEA, calcium imaging, electrophysiology) is a plus Competitive applicants must have
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to develop an autonomous platform for organoid culturing and analysis to address key challenges in organoid-based experiments. This interdisciplinary project will leverage robotic systems for high-throughput