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, you must have a PhD in a relevant field. As a suitable candidate, you have expertise in deep convective cloud processes and experience with scientific data analysis. Prior experience in applying machine
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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of climate model output by means of classical statistical and machine-learning methods #coordination of scientific workflows among project partners Your profile #Master's degree and PhD degree in meteorology
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experience and enhanced potential to receive an ERC Starting Grant in the future. Open to both PhD (natural sciences) and MD (medical sciences) holders. From a variety of academic backgrounds: molecular
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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice
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profile PhD (awarded within the last 5 years) of high quality, ideally in machine learning (in the broad sense), complemented by a strong mathematical foundation in probability and/or statistics Research