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) Interest or experience in multi-modal data integration and trajectory inference Proficiency in Python and/or R Interest in developmental and human biology is highly valued, but not required Strong
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. Hands-on experience with quantitative microscopy (live-cell imaging, high-content screening, or single-cell/single-bacterium imaging) Comfort with image analysis pipelines and scripting (Python
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Strong computational and analytical skills, including solid software engineering ability (e.g., OCaml, Rust, C/C++, Python, or other functional languages) Strong publication record (relative to your
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skills. Prior experience with TFLN is desirable but not mandatory. Strong simulation skills in Python, Matlab, Labview, Inventor, Illustrator, Tidy 3D, CST Microwave studio and Comsol. Analytical
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research; Experience with quantitative data analysis and proficiency in one or more programming or scientific computing environments, such as Python, R, or MATLAB; A strong publication record appropriate to
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mathematics, computational neuroscience, robotics, physics or a closely related field; Strong publication record in relevant venues; Solid programming skills in Python and a modern deep-learning framework
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physics, engineering, computer science, or a related field. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D
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for tomography, laminography, or a closely related modality. Strong scientific programming in Python and experience with GPU processing of large-scale datasets. Excellent written and oral
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in at least one programming language for geospatial analysis: Python (rasterio, geopandas, scikit-learn), R or Google Earth Engine. Experience with open-source tools (QGIS, GRASS GIS, GDAL) and
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include: Strong programming skills, preferably in Python and/or R, with experience building reproducible data-analysis workflows. Excellent quantitative and statistical reasoning, including a clear