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candidate eager to operate at the interface of molecular biology, neuroscience, and AI. Responsibilities Wet-Lab & Experimental Work Set up and optimize imaging based spatial transcriptomics protocols. Set up
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
interested in recruiting faculty members who use and develop artificial intelligence methods and mechanistic mathematical models to address fundamental questions in biology. Examples of research topics include
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and optimizing CRISPR/Cas9 delivery methods. You will also assist the maize transformation team when needed and work closely with colleagues in the lab and in our greenhouses facilities located in
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and optimizing CRISPR/Cas9 delivery methods. You will also assist the maize transformation team when needed and work closely with colleagues in the lab and in our greenhouses facilities located in
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biomedical sciences.Direct access to VIB’s Data Core services, which provide expert support in data management and high-performance computing, including optimized pipelines and large-scale GPU resources.A
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biomedical sciences. Direct access to VIB’s Data Core services, which provide expert support in data management and high-performance computing, including optimized pipelines and large-scale GPU resources. A
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management and high-performance computing, including optimized pipelines and large-scale GPU resources. A competitive salary and benefits package, with relocation support if needed. The chance to make a
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and deep learning techniques. About the role Your role will include: analyzing image-based plant phenotyping datasets (RGB, hyperspectral) designing and optimizing data analysis workflows for automated
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well as public databases. Expertise in the data analysis of one of the modalities that we will use in this project (single cell transcriptomics, ATACseq, proteomics or long-read DNA or RNA sequencing) will
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discovery project teams, owning strategy, prioritization, and key go/no-go decisions – from hit finding to early lead optimization, towards valorization Drive the integration of computational