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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
scales. The objective of this project is to explore a new simulation methodology based on neural surrogate models. By incorporating a “memory effect” into numerical solvers, our approach aims
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International Centre for Translational Eye Research / Institute of Physical Chemistry, Polish Academy of Sciences | Poland | 6 days ago
genetic engineering. Candidates should be familiar with single-cell and/or spatial RNA-sequencing and mice models to develop multi-omics method for profiling of intraocular tumours. The successful candidate
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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, sustainability objectives and future uncertainties into a unified decision-making framework. The candidate will develop probabilistic modelling and stress-testing approaches, including Monte Carlo simulations and
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of XID+, a world-leading modelling framework developed at Sussex, which can disentangle emission from multiple galaxies that appear blended together in observations. The PhD project will focus on advancing
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made large-scale models available to the public, which enables the integration of AI in everyday objects and tasks. As a result, the model scale is doubling yearly, causing a proportional increase in the
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with simulated data and 2D and 3D imaging data of plant tissues (the thale cress Arabidopsis and the aquatic fern Ceratopteris). At a later stage, the models can be extended to three dimensions using
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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, including industrial applications in geology. Your immediate leader will be the Head of Department. Duties of the position Simulate MRI data and model disturbances arising from various physical sources
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect