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focuses particularly on understanding how the tumor microenvironment (TME) undergoes architectural remodeling – including changes in extracellular matrix composition, tissue mechanics, and spatial
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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the molecular composition of a single cell in space and time. These models will describe high-dimensional systems with many interacting components and may use multiscale and coarse-grained representations
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emphasis will be placed on personal characteristics such as creativity, thoroughness, a structured approach to problem-solving, and the ability to work both independently and in a team. Additional
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, thoroughness, a structured approach to problem-solving, and the ability to work both independently and as part of an interdisciplinary team. Additional qualifications Experience in simulation-based or likelihood
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structure – function relationships in lipid systems for drug delivery (predominantly lipid nanoparticles). The group has extensive expertise using large scale research infrastructures to study lipid systems
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identity and extracellular signaling, but how cell types, signaling mechanisms, and transcription factors jointly determine tissue structure is incompletely understood. The Koplev lab is recruiting
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external large-scale datasets. The role focuses on establishing the data foundations for the program’s initial modelling efforts by identifying, evaluating, integrating, and structuring large biological
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model organisms, their application in conifers remains limited due to challenges associated with tissue structure, nuclei isolation and sensitivity to inhibitory compounds. This project aims to develop