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for imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing
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scattering with machine-learning models trained on plant mutants, the project will shed new light on the cellular-level biochemistry that governs plant growth, development, and stress responses. This is a
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a pioneer, harnessing state-of-the-art Omics technologies to dissect fundamental biological mechanisms, power large-scale supervised and foundational machine learning initiatives, and comprehensively
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architecture, testing/debugging skills, and version control. Experience applying machine learning, artificial intelligence, or statistical modelling techniques to digital forensics, online surveillance, or large
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of the Recovery and Resilience Program, through the Foundation for Science and Technology – FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering (Medical Informatics
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undergraduate or graduate students on research projects Proficiency in advanced statistical modeling, including structural equation modeling, longitudinal analysis, network analysis, or machine-learning
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: • Backend based on REST/GraphQL APIs that expose cork stopper catalogue functionalities, creation and management of final products, and consultation of machine learning model records; • Angular frontend
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. This recommendation system should be designed based on current machine learning and artificial intelligence strategies, allowing it to adapt to each user's profile and the different types of data collected by
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/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object detection, segmentation, and multi
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development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning; scientific simulation; or theoretical astrophysics Effective oral and