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. Responsibilities include designing, performing, and optimizing experimental workflows, including tissue preparation and sectioning, antibody-based assays, and highly multiplexed immunostaining and microscopy, as
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future. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular
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Giacomello. Examples of tasks: Design, perform, and optimize experimental workflows for ST, SmT and single-cell multiomics Prepare and process animal and plant tissue samples for spatial and sequencing-based
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on methodological development in cryo-electron microscopy (cryo-EM), particularly in image reconstruction and 3D volumetric analysis of macromolecular structures. Rather than aiming to incrementally optimize existing
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-Driven Life Science (DDLS) uses data, computational methods, and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human
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, providing a platform to identify and optimize therapeutic candidates. Our group specializes in developing technologies to assess the multicellular environment within three-dimensional microtumor models