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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
Text-to-Video Generation Models Supervision : Dr Stéphane Lathuilière (INRIA-UGA) Funding : BPI contract Contexte : Recent advancements in generative AI, and in particular diffusion models [1,2], have
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Eligibility criteria The candidate must have a strong background in materials physics, atomistic simulation, or numerical modeling. Experience in molecular dynamics and handling force fields is required
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 2 months ago
position on ocean modelling within the ERC Stuod (Stochastic transport in ocean dynamics). Odyssey (for Ocean DYnamicS obSErvation analYsis) is a recently created team involving researchers from Inria
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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different contexts, and test whether these decisions rely on shared neural representations. The postdoc will combine behavioural experiments, magnetoencephalography (MEG), computational modelling of behaviour
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models, whilst respecting the specific spatial structure induced by river networks. - Cleaning, structuring and analysing historical long-term monitoring data (approx. 200 sites). - Adaptation and
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heat loss. Common thermal insulation materials are highly porous and contain open or closed pores filled with gas. In 2021, only 10% of insulation materials were derived from renewable resources. Plant
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the past few decades, remarkable progress has been made toward constructing functional quantum processors. However, to build fully capable quantum computers and networks, a wide range of complementary
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The successful candidate will contribute to the development of a new generation of 3D-printed biomimetic materials designed to model the human intestine in vitro, as part of the BAM-INT project. The project aims
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental