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Field
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algorithms to shape the liveable cities of tomorrow? Job description Human-centred AI techniques, such as Reinforcement Learning from Human Feedback (RLHF), hold great potential for supporting design
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for ambitious scientists seeking to transition to independent research leadership while contributing to cutting-edge, interdisciplinary projects at the intersection of AI Virtual Cell modeling, Epigenetics, and
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at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 9 days ago
) Optimization and parameter identification methods Data-driven modeling and machine learning Physics-Informed Learning (or hybrid modeling approaches) Handling and analysis of large-scale datasets (e.g., mobility
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) Experience in genomics, single-cell data, or machine learning (preferred) Why this is exceptional Build next-generation AI models of the human genome Work with one of the richest longitudinal PD datasets
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with contemporary machine learning methods. We are looking for an ambitious Postdoc who will lead our efforts on the design, implementation, and training of mechanistic models of cell organization. In particular, we
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technology (PAT) adds cost, time, and introduction of human error. This project aims to develop a novel, label-free microbial detection method, coupled with machine learning tailored for cleaning validation
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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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at the systems level. By integrating groundbreaking multiomic methods at the single-cell level with advanced experimental models, such as human organoids, the laboratory maps the molecular and cellular mechanisms