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interdisciplinary, and together we contribute to science and society. Your role We seek a highly motivated AI scientist, biostatistician or computational biologist who is well versed in the statistical and machine
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their statistical extensions, the deep latent variables models (DLVM) [1], allowed clear advances in Artificial Intelligence in the last 5 years, they clearly suffer from an overall weak knowledge
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collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment-associated alterations in
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their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information will be kept confidential and will not be used
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SD-26085 POSTDOCTORAL RESEARCHER IN THE OXIDATIVE CHEMICAL VAPOR DEPOSITION OF FUNCTIONAL BUILDIN...
their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal opportunities in our workforce. This information will be kept confidential and will not be used
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: Laboratory related techniques: HPLC, spectro-photometry, ELISA (markers inflammation, oxidative stress…), particle-size analysis, cell-culture studies, etc.; Interpret findings statistically; Participate in
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statistics publications per professor over the last 5 years. It provides an international and stimulating environment with regular top research seminars and several top-quality conferences. It also benefits
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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research methods Experience in applied social science statistics is an asset Capacity to work to with interdisciplinary collaborative teams Capacity to work with public stakeholders in the educational field
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the understanding of deep latent variables models for unsupervised learning with massive and evolving heterogenous data. Although deep learning methods and their statistical extensions, the deep latent