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virtual reality development, machine learning, or advanced data analyses and modeling are highly desirable. Position Status Full Time Posting Number 25FA0682 Posting Open Date Posting Close Date
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 19 days ago
the machine learning community as challenging, high-dimensional testbeds. Notably, the recently developed WOFOSTGym simulator \cite{solow2025wofostgym}, bridging crop modeling and RL, received the Outstanding
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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- 4 Additional Information Eligibility criteria Required skills: strong experience in TVB modeling, experience in fitting models to human data, strong level of autonomy, solid knowledge of machine
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user-friendly tools that will be used by
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communication skills. • Track record of conducting original research and publishing in peer-reviewed scientific journals. Preferred Qualifications • Experience in remote sensing or machine learning applications
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engineeringEducation LevelPhD or equivalent Skills/Qualifications We are seeking a scientist with: Expertise in image-based biological tissue modeling and simulation Good command of deep learning Expertise in coding and
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks