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(e.g. Interspeech, ICASSP, SSW) and contribute to open-source release of corpus and models. Qualifications Requirements A doctoral degree in speech technology, machine learning, computational linguistics
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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cleaning and quality control, supervised and unsupervised machine learning, parametric and nonparametric statistical methods, deploying production models, and assisting with the communication of scientific
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, scalability, and effective performance across university use cases. Develops, trains, and fine-tunes machine learning models for a variety of university applications. Conducts experiments to evaluate model
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data output by using appropriate computer language/tools to provide technical solutions for moderately complex application development tasks. Document code and associated processes by adhering
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Qualifications: M.S. (or equivalent professional degree) in physics or a related field, and experience with different computer environments and languages especially Python, Matlab, IDL, FORTRAN and UNIX/LINX
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the interplay between mutations, energetics, and evolutionary constraints, including epistatic effects. · Developing or applying machine learning approaches to predict or redesign frustration patterns in proteins