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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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R or equivalent skills in another relevant language. We are not expecting you to be an expert in all forms of computer simulation, Large Language Models, or machine learning etc, but a working
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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Sciences, who develop, manage, and refine machine learning techniques for identifying copyists; Participation in the dissemination of research results through presentations and publications; Data management
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decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis. Candidates must
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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following areas: Mathematical Analysis/ Numerical Analysis/ Theoretical Machine Learning Please note: Applications from candidates with degrees in other disciplines (e.g., Computer Science, Engineering) will
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(s). Knowledge of pulse generators, oscilloscopes, multimeters, power supplies, diode lasers, telescopes, cameras, range finders, lathes, milling machines, 3D printers, band saws. Salary: Compensation
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to scholarly peer-reviewed publications. Opportunities exist for the selected applicant to mentor students and to develop learning opportunities (courses, workshops, etc.) for the UK earth science community. The