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architectures), determination of optimal embeddings and encodings for protein structures, multiple alignment methods, Bayesian dendrogram reconstructions, and benchmarking including jackknife resampling. Position
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
and Bayesian methods. Experience using DOE computing allocations (NERSC, OLCF, ALCF). A record of productive collaboration in multi-institutional projects. Special Physical/Mental Requirements Special
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of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim or http://github.com
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. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification
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chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
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recognized research centers and institutes. The incumbent will join a collaborative department with internationally recognized expertise in biostatistics, statistical omics, bioinformatics, Bayesian
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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approaches to data analysis. We welcome applicants working across a variety of approaches, particularly computational and Bayesian, and with a range of data, including text, survey, and panel data. In
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are seeking a PhD candidate to join our young team (Computational Systems Medicine) within the context of the BMFTR-funded DIASyM (https://diasym.mscoresys.de/ ) project, to develop new statistical and
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stochastic processes, Markov models, dynamical systems, quantum walks, or related mathematical approaches. Experience with computational model fitting, Bayesian inference, simulation, or formal model