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Field
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interpretable artificial intelligence (preferred). You have experience with coding in Python using deep learning toolkits (e.g., PyTorch). You have good analytical and problem-solving abilities. You are
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experience with coding in Python using deep learning toolkits (e.g., PyTorch). You have good analytical and problem-solving abilities. You are proficient in English (written and spoken). What we offer We offer
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sciences. Prior machine learning or Python experience is a strong bonus, but not a must. We seek colleagues who enjoy coding, scripting and analytics, and are keen to push the boundaries of data-driven
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | about 1 month ago
: Good programming skills, preferably in Python, and strong analytical abilities. A solid background in machine learning, computer security, privacy, distributed systems, or a closely related area
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excellently equipped lab infrastructures; including a state-of-the-art 3D printer and all analytical facilities needed for your research an inspiring environment with international and ambitious teams ample
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for measurement automation and data analysis Machine learning or Artificial Intelligence methods You enjoy hands-on experimental work, have strong analytical skills and are motivated to develop innovative sensing
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systems training requirements. Ability to work independently and as part of a team. Analytical skills and the ability to resolve technical or research problems and issues and to interpret the acceptability
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coding in Python using deep learning toolkits (e.g., PyTorch). You have good analytical and problem-solving abilities. You are proficient in English (written and spoken). What we offer We offer a doctoral
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, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data, how it can be extracted, and how
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents