Carrières à Sigma Nova

Expert AI: foundation models for the data that runs science and industry We define Expert AI as foundation models built for scientific and industrial data: continuous and irregular time signals, spatiotemporal fields, multimodal scientific recordings, time-stamped event logs from real organizations. The kind of data that doesn’t look like text, doesn’t fit transformer assumptions, and rarely leaves the institutions that hold it. This isn’t a fine-tuning problem. It requires architectures designed for the data, infrastructure designed for how the data lives, and a tech stack of AI components purpose-built to assemble foundation models for new domains. General-purpose AI was never built for science and industry. The data is sparse, noisy, distributed, and governed by strict rules. It demands expert intelligence: models that understand the structure of a domain, not just the surface of its data. Our approach is co-foundation with domain experts: AI and domain science evolving together. We build domain-specialised foundation models trained to master a field’s data, constraints, and logic from the ground up. We work hand in hand with the scientists, clinicians, and engineers who know the domain best. Our models learn from distributed sources through decentralised architectures, keeping sensitive data where it belongs. We anchor in brain and neuroimaging, the most complex signal of all and the hardest class of expert data we know how to characterize. In parallel, our cross-domain transfer programme works on extending the same components and methods to open new application domains. We invest in demonstrators that turn research into tangible proof, working with partners on concrete use cases that test our models in real conditions and inform the next iteration of the science. Our team finished 2nd out of 1,183 in the NeurIPS 2025 EEG Challenge, and we publish at Tier-1 AI conferencesincluding NeurIPS, ICML, and CVPR. This dual focus, rigorous science and applied impact, defines our approach.

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