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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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[map ] Subject Areas: Mathematics, AI-based drug design and discovery, Bioinformatics/Protein Engineering/Single-cell Omics Data, Mathematical AI/Machine Learning/Deep Learning, and Computational
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needs. Project background We are excited to announce an interdisciplinary PhD opportunity focused on mechanochemical processes driving radical formation and redox cycling in the deep subsurface, with
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quantitative field. Strong background and expertise in data science, bioinformatics, network science, artificial intelligence, machine learning, deep learning, or related areas. Solid understanding of AI
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations, scientific publications, and technical reports
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materials property predictions. A deep understanding of materials properties and close connections in academia and industry enable the group to explore exciting research avenues. For more information about
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Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D Imaging, Recommender
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Skills :Programming, Research Knowledge :IA, Deep Learning, Python Where to apply Website https://rubis.univ-spn.fr/offres/voir/206 Requirements Specific Requirements Be a graduate of an Engineering