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18 Sep 2026 Job Information Organisation/Company UNIMORE Research Field Engineering » Computer engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage
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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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based on Artificial Intelligence, Machine Learning and Data Science for the modelling, analysis and interpretation of complex biomedical systems. Research activities will include the design and validation
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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn. One of the best college towns and best places to live in the United States, Chapel Hill has diverse social
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such as Python and/or R, with experience analyzing large-scale molecular, clinical, or biomedical datasets. Experience developing or applying machine learning, statistical modeling, or artificial
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patients with pancreatic cancer and other aggressive tumor types. The work includes statistical modelling, machine-learning and systems biology approaches to identify drivers of metastasis and treatment
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members to conduct novel research. Key Responsibilities Produce novel computational models of biological systems (e.g. MuJoCo, MJX, Blender, aerodynamic modeling approaches) 30% Apply machine learning and