Ella Linxia Müller
Research Fellow · Computational Biology
Swiss Federal Institute of Technology in LausanneAbout
Ella Linxia Müller is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Swiss Institute for Experimental Cancer Research (ISREC), specifically in the UPGON laboratory led by Prof. Pierre Gönczy. She is enrolled in the Doctoral Program in Computational and Quantitative Biology (EDCB), reflecting her interdisciplinary training at the interface of life sciences and quantitative methods.
Her academic background is rooted in a strong foundation in life sciences and computational approaches, as indicated by her enrollment in a specialized doctoral program integrating biology with data-driven methodologies. The program emphasizes rigorous research training and cross-disciplinary collaboration.
Ella's research interests lie at the intersection of computational biology and quantitative cell biology. Given her affiliation with the Gönczy group, which investigates mechanisms of cell division and development using advanced imaging, genetics, and systems-level analysis, her work likely involves modeling biological systems, analyzing high-throughput data, and understanding developmental dynamics through quantitative frameworks.
While no specific publications or scientific awards have been listed in the provided information, her position within a leading research group at EPFL suggests active engagement in cutting-edge scientific inquiry. The Gönczy lab is known for its work on centrosome biology, asymmetric cell division, and systems-level understanding of development, indicating that Ella's research contributes to these broad fields.
Ella Linxia Müller is not currently listed as an advisor to any students. There is no information available regarding grants she may be involved with. She is part of the UPGON research group at ISREC, EPFL, which focuses on understanding the fundamental mechanisms of cell division and embryonic development using a combination of genetic, cell biological, and computational approaches.
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