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linear ballistic accumulator models, diffusion models, biased competition models, or Bayesian models. During the employment, the candidate is expected to engage in the development of computational models
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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functional data ”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of molecular data in cancer genomics. The position
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(classic; Bayesian), machine learning, or other statistical approach with accompanying expertise in whatever stats package(s) is desired (SPSS; R; Stata; SAS; NumPy or PsyPy; etcetera). A strong ability to
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learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
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statistical approaches. A fundamental understanding of Deep Neural Networks as applied to high-frequency time series datasets, including the ability to design and implement custom NN models in PyTorch, as
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• Skilled in single-cell/population data analysis (e.g., GLMs, decoding) Preferred Qualifications • Background in machine learning or computational modeling (Bayesian methods, neural networks, etc
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projects. Expert supervisors and mentors from DTU and Novo Nordisk. Opportunities to participate in conferences, symposia, and networking events to share and enhance your research. Your role will be pivotal
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Department of Ecology We are looking for a postdoc/researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoc