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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of additively manufactured Mg alloys for biodegradable implant applications. Host: Helmholtz-Zentrum Hereon
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4 fully funded PhD student positions in the computational, mathematical & experimental plant science
. They apply and develop a broad range of interdisciplinary technologies ranging from genetics and genomics to structural biochemistry, advanced imaging and computational and mathematical modelling in various
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block within this process. You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to develop the deep-learning models while having
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systems and in vivo models in combination with cell state and mutation reporters and single-cell technologies with spatial readouts to study the impact of intestinal microbes and their metabolites on colon
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infection biology, immunology, parasitology and medical biotechnology The following 6 PhD projects are available in 2026: Innate immune recognition in the gastrointestinal epithelium – Organoids as host model
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transcriptomics (Visium HD, Xenium), single-cell RNA sequencing, and computational modeling, the student will map tumor cell states, lineage hierarchies, and differentiation trajectories. Insights from
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partners. Through national and international collaboration, conference participation, and publication in peer-reviewed journals, you will build a strong scientific profile. The program is highly research
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well as emerging materials. These experimental techniques will be complemented by advanced electromagnetic modelling. Responsibilities and qualifications The ideal candidate will possess demonstrated expertise in
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resolution across large genomic datasets. We focus on cancer models (osteosarcoma, breast cancer, leukemia) and on neural progenitor cells to understand how genome instability contributes to tumor initiation
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assessment of chemical plants using HAZOP analysis Use of process modeling and simulation to enhance quantitative assessments Use of machine learning to support HAZOP discussions with the aim of obtaining a