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proteomic analyses often obscure the intricate cellular and molecular variations within this complex environment. Single-cell proteomics (SCP) offers a novel approach to dissect this heterogeneity by
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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Performs a variety of moderately complex research tasks determined by the field and scope of the particular research project/study. Performs tasks related to the research project independently, but
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that will be affiliated with one of six possible multidisciplinary projects. The ideal postdocs will have expertise in some of the following areas: computational modeling, computational biology, computational
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prostate tumor samples. This position requires a strong background in both experimental proteomics and computational data science (R and Python), with an emphasis on LC-MS/MS workflows and long-term cohort
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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to self-organize into complex structures. Our approach is to develop sophisticated mathematical models – informed by state-of-the-art biological knowledge and experimental data – to understand
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pores, and phase boundaries establish localized environments that concentrate activity, enhance specificity, and scaffold complex regulation. These interfaces don't just divide—they integrate: shaping
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the view of physics aiming for identifying principles behind complex systems. We collaborates closely with engineers, immunologists, and clinical microbiologists on campus. Within this project we will