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Field
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called syncytiotrophoblasts are subsequently damaged, by the altered haemodynamics, however the molecular mechanisms are not fully understood and the impact on haemostasis remains unclear. We aim
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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 is connected
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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an efficient AI foundational exploration of the molecular space. How can we bias the generative models towards desirable molecular properties How can we integrate generative AI models and different molecular
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adults, characterised by severe genomic instability and a lack of targeted therapies. Using cutting-edge methods and unique primary patient-derived cell models, this project will seek to understand
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molecular biology, quantitative imaging and biophysical approaches to investigate cell shape changes in cultured cells and in vivo. Current projects in the lab include investigating the regulation
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sensitivity and respect for diversity Desirable: Experience in cheminformatics, molecular modeling, or enzyme mechanisms Familiarity with databases, data mining, and knowledge management Why Join Us: Be part of
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soon as possible at Department of Cardiology, Angiology and Pneumology. The research group "Molecular and Translational Cardiology", headed by Prof. Dr. Patrick Most and Dr. Julia Ritterhoff, focuses
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-ethnic environment with sensitivity and respect for diversity Desirable: Experience in cheminformatics, molecular modeling, or enzyme mechanisms Familiarity with databases, data mining, and knowledge
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to probe metabolic states and function of control mechanisms. Analysis of strain performance and control mechanisms using computational tools (e.g., flux balance analysis, kinetic models, network component