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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
the start date, a PhD in Data Science, Computer Science, Electrical Engineering, Electrical and Computer Engineering, or a closely related field. The applicant’s research should focus on machine learning
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in tropical regions; analyze links between macrofauna and soil carbon; build/validate scoring algorithms using machine learning/cumulative functions. Outputs – Lead scientific, technical, and policy
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criteria We seek a researcher with strong machine learning modelling expertise with experience in the analysis of challenging large-scale data sets. Experience with cellular imaging data or virology
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networks, from a machine learning and information theory perspective. This basic research project has strong translational potential and aims to elucidate how immune function is altered during sepsis, with
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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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deep learning, and its extensions for these additional targets. In particular, we have a large collection of newspaper articles dealing with migration-related topics, and we are investigating how text
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of Singapore, and EPFL (Switzerland). These partners are looking for talents in several domains of machine learning, AI, computational biology, and biology, to develop PhD theses across the main pillars
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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Science in Earth Observation develops innovative signal processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth
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, implement and benchmark machine learning models for large-scale health datasets consisting of diverse information including structured medical history, demographics, clinical notes, laboratory measurements