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are particularly interested in candidates who combine computational biology, data science, and machine learning/AI with deep biological insight. While wet lab activities are welcome, they are not mandatory. However
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, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it), Machine learning Genoa Center, Italy. Context and Post-doc objectives Conventional optical microscopy
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surfaces. Consequently, it is essential to develop mobile measurement instruments and acquire comprehensive datasets to validate and enhance the models. This PhD thesis project, a collaboration between COLAS
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machine learning. We particularly value depth of knowledge, originality, and the potential for cross-disciplinary innovation. Relevant application areas may include (but are not limited to) natural
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techniques for data storage/retrieval/processing/visualization on large scale. Cybersecurity Software engineering Machine/deep learning Technical aspects of human computer interaction (HRI, multi-modal
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properties changes. - The demonstration of the tear detection with machine learning classification applied directly on S-parameters of the MWI system without solving the inverse problem. The objective
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simulation tools and machine learning techniques. Initially, the work will be based on the design of a database of medical images, which will be processed using convolutional neural networks to identify design
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repeatability and accelerate the generation of relevant experimental data for machine learning. Numerical component The research engineer will contribute to the enrichment of an existing database, relying
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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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properties are optimized by machine learning algorithms to retrieve properties of reference flames gathered in a learning base. A virtual scheme consists of a main block that models the heat release from