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Statistical Signal Processing, Data Science, Machine Learning with an interest in astrophysics - or a PhD in Astroparticle Physics with skills and professional experience in experimental data analysis. Website
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reliability. · Understanding of hardware accelerators for AI and their operation. · Familiarity with machine learning workloads (e.g., CNNs). As this is a research position, it is necessary
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l'institut du thorax, INSERM, CNRS, Nantes Université | Nantes, Pays de la Loire | France | about 2 months ago
LevelPhD or equivalent Research FieldMathematicsEducation LevelPhD or equivalent Skills/Qualifications Must hold a Ph.D. degree in Mathematics / Computer science or Machine Learning. • Be able to work within
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project combines techniques from machine learning, natural language processing (NLP), and knowledge representation to support legal scholarship and decision-making. The position entails close academic
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, decision-making and control using data, have been proposed. For control or management applications, reinforcement learning (RL/DRL), a branch of machine learning, is a promising solution that involves
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solid experience in programming, particularly in Python and JavaScript. Significant experience in data science and machine learning will be highly valued. You like to work in a team while demonstrating
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(including computer science, machine learning or deep learning). Activities Description of the research activities : The post-doctoral researcher will develop the research actions defined in his/her research
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Postdoc Positions Country France Application Deadline 1 Jan 2026 - 12:00 (Europe/Brussels) Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 3 Jan 2026 Is the job
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Université de Bordeaux / University of Bordeaux | Villenave d Ornon, Aquitaine | France | 2 months ago
genomic diversity and genetic load from available genomic data for various woody species. For this, you will use the available pipelines from phase 1 of the project and apply machine learning-based methods
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and