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asynchronous AI-led chemical optimisation across chemistry laboratories¿. This role sits at the intersection of robotics, machine learning, and chemistry, aiming to develop robotic systems that work
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and summarize results, publish in scientific journals, and contribute to the supervision of students. Your profile PhD degree in bioinformatics, machine learning, computer science, biochemistry
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 6 days ago
/Qualifications Technical skills and level required : mathematics, programming Languages : English Relational skills : good communication skills Specific Requirements The candidate must hold a PhD in machine
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fellow devotes most of their time to research. There is the possibility of teaching up to 20%. Requirements PhD degree in machine learning, automatic control, system identification, signal processing
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to leverage machine learning approaches for the optimization of polymer properties and degradation profiles. The successful candidate will lead pioneering research in controlled polymer synthesis, employing
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to independently drive research projects, analyze and summarize results, publish in scientific journals, and contribute to the supervision of students. Your profile PhD degree in bioinformatics, machine learning
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intensity shift and vary over time. Rather than treating nationalism solely as a political ideology, we analyze its practical operations and its effects on the lives of different groups. RESPONSIBILITIES We
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effects on the lives of different groups. RESPONSIBILITIES We seek postdoctoral researcher with expertise in cultural memory studies and nationalism research. The postdoctoral researcher will carry out
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Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data