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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 | 13 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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of Iowa Health Care is seeking a postdoctoral research scholar in the Ignacio V. Ponseti Biochemistry & Cell Biology Laboratory (PI: Dr. Hongshuai Li. https://medicine.uiowa.edu/orthopedics/profile
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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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to assist in research using molecular techniques. Prior experience with isolation/culture of neural cell types, design and validation of molecular constructs for genetic modification of cell lines, and single
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of life science research experience. · Experience working with mouse models of disease. · Technical aptitude for learning and executing complex experimental techniques for molecular biology, cell-based
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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