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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 19 days ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine
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applicants for a 6-month paternity leave replacement who have a strong interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental
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targets specifically amyloidogenic proteins implicated in the diseases like Alzheimer’s and Parkinson’s. Required qualifications A PhD. In the area of synthetic/ medicinal chemistry is rquired. Experience
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detection framework for tipping points. Contribute to the design of scalable and interpretable forecasting strategies for large climate simulators, integrating adaptive sampling and Bayesian techniques
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with a strong background in molecular virology, next-generation sequencing, Bayesian analysis, phylogenetic analysis, statistical genetics, and the ability to use R and/or UNIX/command line applications
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to identify and promote the voluntary adoption of effective bycatch reduction strategies in targeted fisheries. There will also be opportunities to contribute to writing research proposals and mentoring
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA
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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