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observation and/or handling large data sets are encouraged to apply. Prof. Miller in observational time-domain astronomy, focusing on the study of Type Ia supernovae while working to combine observations and
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 11 hours ago
Experiment (AGAGE) and a large atmospheric chemistry group at MIT. The work will focus on interpretation of AGAGE observations of greenhouse and ozone-depleting gases using models, measurements, and inverse
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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph
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research funding than all other New Jersey universities and colleges combined. Rutgers manages Protein Data Bank (PDB), the global archive of 3D structure data for large biological molecules (proteins, DNA
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techniques (e.g. explainable, ethical, empathic and agentic AI), natural language processing (NLP), large language models (LLMs), data science methods, and mHealth to analyze large-scale, multidimensional, and
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disease dynamics, preferably with applications to emerging or re-emerging diseases; Proficiency in programming languages such as R, Python, MATLAB, C++, or similar tools for data analysis and model
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description: AG Weinelt is searching for a postdoc in the field of Ultrafast Spin and Electron Dynamics, who is experienced in performing time-resolved X-ray spectroscopy experiments at large-scale facilities
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are seeking a postdoctoral researcher specializing in digital technologies and urban health. The successful candidate will develop innovative solutions based on the analysis of urban data (big data, IoT, GIS
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candidate with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental