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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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biomarkers – including voice biomarkers – to support the early detection and remote monitoring of psychological well-being in people with diabetes. This project builds upon the large international Colive Voice
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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diabetes, pancreatitis, and pancreatic cancer—conditions that are epidemiologically linked, though their molecular interplay remains unclear. Pancreatic cancer, one of the deadliest solid tumors with a five
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used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore
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evidence that sustained regenerative capacity is possible, even in severe conditions such as Duchenne muscular dystrophy. However, the mechanisms underlying EOM resilience remain largely unknown, making EOMs