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experimental optimization, structure–performance analysis, and/or mechanistic investigations. Familiarity with machine learning techniques, optimization algorithms, and data-driven approaches for materials
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in physics-informed machine learning, surrogate modelling, generative AI, or design optimisation would be desirable. An entrepreneurial mindset and a willingness to build commercial acumen alongside
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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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, telecommunications, or a related field (with a CGPA of at least 4/5) Experience (e.g. coursework, projects, etc.) in AI and machine learning, including understanding of various DNN architectures, training models, and
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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