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proven practical experience in the implementation of machine vision systems Fluent in English, for both written and oral communication Enthusiastic team player Openness to learn the basics of plant growth
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are expanding our mission to harness the power of artificial intelligence for life sciences research, innovation, and impact. We are now looking for an experienced Machine Learning Expert to establish and run a
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of Applied Engineering is looking for a full-time (100%) doctoral scholarship holder in the field of in-air acoustic sensing and applied machine learning for building the next-generation of intelligent robotic
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computer scientist who can lead a research group where the development of new machine learning techniques serves as an important basis for tackling challenging biotechnological issues. Your achievements in
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Educational Master – and conducts research across a wide variety of domains in each of these fields. The faculty’s vision can be summarized as: “With trust, in connection, through continuous learning.” The FPPW
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), user interface design, or data visualization techniques. Familiarity with frameworks for explainable machine learning (e.g., SHAP, LIME, Captum, Alibi). Experience in designing context-aware, adaptive
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experience in common deep learning frameworks (e.g., PyTorch and TensorFlow) would be a benefit; The qualities to carry out independent research, demonstrated e.g., by the grades obtained in your (under
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to ensure the learning of the essential basic concepts of fundamental immunology in the second year, as well as the knowledge and understanding of the host–pathogen relationship in the third year of
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. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high
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natural and human disturbances through climate-smart forestry startegies, based on observations and predictive models. Where to apply Website https://unimol.concorsismart.it/ Requirements Additional