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Field
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technologies (e.g., spatial transcriptomics, CODEX, Akoya PhenoCycler). Proficiency in single-cell RNA sequencing, high-dimensional data analysis, bioinformatics tools, and programming languages (R, Python
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in spatial equilibrium modeling (e.g., through coursework, dissertation work, or previous projects) experience with spatial data analysis using GIS tools or spatial libraries and familiarity with
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harmonic generation. The project involves pulse compression in a multipass cell, pulse shaping in both time and frequency domains, beam shaping using spatial light modulators (SLMs), and generation of new
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electrophysiology and sleep) and (ii) in spatial transcriptomics and high-resolution imaging. The selected candidate will work at the intersection of molecular biology and systems neuroscience, gaining a unique
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This project aims to identify the spatial genetic and adaptive dynamics of tumour microbiome in colorectal cancer. To do so, we will integrate single-cell genomics, transcriptomics and clinical data
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. Required Qualifications: A doctoral degree (PhD, MD, or equivalent) conferred by the start date. Proficiency in R/Python Experience with scRNAseq, and/or spatial proteomic/transcriptomic data analysis Growth
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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, contributing to the development of innovative spatio-temporal analysis and engineering challenges associated with uncertainty. The role supports collaborative investigations, model development, and experimental
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of metabolic states in retinal ganglion cells; spatial transcriptomic and correlated metabolic analysis of retinal ganglion cells; treatment of retinal ganglion cells in models of degeneration; data collection
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complex endometrial models and optimizing in vitro implantation assays. Culturing human embryos and generating stem cell-based embryo models. Tissue sectioning for advanced spatial transcriptomic analysis