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algorithms and parallel/distributed variational algorithms in AI/ML for application workflows and large-scale HPC and QC systems Develop quantum machine learning (QML) algorithms for optimization of multi
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 8 days ago
to compare base approaches (no active learning) with different state-of-the-art active learning algorithms including our solutions; Participate to the redaction of a publication exposing the methodologies and
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demonstrated ability to apply these techniques in the biomedical or health informatics domain. Strong algorithm development and programming skills are required, along with excellent teamwork and communication
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well as in the design of machine learning algorithms (ANN, SVM, Decision Tree, and Random Forest) applied to healthcare, will be particularly valued. Proficiency in programming tools (Matlab) and statistical
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. The roles of this position include: Development of algorithms to improve memory/sample/time efficiency of LLM training. Development of a workable prototype system with capabilities such as conversational
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, and assess irrigation investments. As part of the IrrEO project, the PDRA will lead the development and validation of national-scale irrigated area and agricultural mapping algorithms in two
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for STEM education, developing or updating curriculum for robotic learning. The project works on the development hardware & software for the robot, design the algorithm with the embedded, computation sensors
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understanding of machine learning/deep learning fundamentals. Able to appreciate and explain the mathematical workings of common algorithms for computer vision/NLP/tabular data. Hands-on skills in Python-based AI
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environmental engineering (candidate status) and a demonstrated record as a lead author on publication(s) using machine learning algorithms to forecast water quality. Fluency with open source coding languages (R
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fait appel à des « pipelines » d'analyse qui associent les technologies de séquençage innovantes aux algorithmes bioinformatiques pour assembler, annoter et comparer les génomes viraux. Le projet