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to learn new methods and domains and to transfer approaches across them. The role centers on developing modular, acquisition-aware AI foundation models and applying them to large-scale scientific and
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. This postdoctoral fellowship in the Arlotta lab involves computational analysis of large, multimodal single-cell datasets, as part of a collaborative project which also aims to generate AI and machine learning models
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
and Objective: Tabular and Time-series foundational models have become very popular due to their high accuracy relying solely on In-Context Learning (ICL) [3, 4]. However, one still requires training
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insights into actionable strategies for pediatric care. Our work combines statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data
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discrete choice modelling, behavioural data science or machine learning? Are you interested in developing the next generation of AI tools that accelerate scientific discovery while maintaining behavioural
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. The research project focuses on the design, development and validation of Artificial Intelligence and Machine/Deep Learning models applied to healthcare, with particular reference to computer vision applied
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modeling of complex, multi-site datasets. In this role, you will bridge the gap between complex data infrastructure, cutting-edge machine learning, and synthetic data generation—building automated
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Principal investigators or co-principal investigators as a Jr. Data Researcher for a wide range of projects and activities involving machine learning (ML) and artificial intelligence (AI), including autonomy
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cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to effectively
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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations