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project, an ARIA-funded collaboration between EPFL and Imperial College London, aims to build a formally-verified ML inference engine, demonstrating that AI can help make verified systems competitive
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prototype into a working MVP fast enough to support the next grant application round. Main duties and responsibilities Rebuild the RVI scoring engine as clean, standalone Python code, implementing the
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engine, demonstrating that AI can help make verified systems competitive with unverified systems in terms of development effort, features, and performance. Main duties and responsibilities Conducting
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, deployable tool. Main duties and responsibilities Extract and harden a scoring engine from an existing Python/Streamlit prototype into robust, standalone code Rebuild the data layer from Neo4j to a Python
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verifiable tasks for mathematics, code, reasoning, or tool use. Familiarity with lower-level GPU/distributed libraries such as NCCL, Transformer Engine, FlashAttention, or communication backends
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; experience with Google Earth Engine is a plus; Experience with open-source tools (QGIS, GRASS GIS, GDAL) and spatial databases. Soft Skills Scientific rigor and autonomy in conducting research; Ability to
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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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-based data, physiological signals, movement tracking, or related complex datasets. Some knowledge of Unity game engine development and experience with network programming with C# or equivalent, at