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systems, understand the design and development decisions that propagate social biases, and develop theoretical and algorithmic approaches to mitigate them. Key responsibilities include developing bias
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del Sol, which develops computational models to address challenges in stem cell research, aging and disease modeling. The group employs methodologies from different areas of mathematics, engineering
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
, b) strong communication skills – written and oral, c) ability to develop/translate model algorithms and develop new model code in Fortran, d) software skills needed to work with multiple observed and
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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knowledge and fosters the development of highly skilled researchers and professionals. Our research focuses on material properties and manufacturing processes for mainly metallic components, specifically cast
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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that enable material traceability and circularity in plastics. The role focuses on developing and curating Deep-UV spectral databases, designing AI-based classification models, and further advancing
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contribute to the development of fundamental aspects of computer science (models, languages, methodologies, algorithms) and to address conceptual, technological, and societal challenges. The LIG 22 research
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disease progression. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will