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PhD; Excellent master's degree in computational science, physics, mathematics, chemical engineering, or a related discipline; Experience in mathematical modelling and numerical methods for ordinary and
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Strong background in computational science, applied mathematics, or computational biology Ideally, familiarity with numerical methods for PDEs (e.g., finite difference, finite element) HPC experience is a
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modelling and numerical methods for ordinary and partial differential equations; Strong interest in working in a cross-disciplinary, collaborative project at the interface of electrochemistry and mathematical
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on microfluidic and physical principles, fabricate & optimize using state-of-the-art microfabrication techniques and characterize chip performance (fluidics, mechanics, and reproducibility). Method development
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Group in the Social Networks Lab at ETH Zürich is developing network data science methods and applying them in a variety of domains. Our current research focus is on sports analytics and we seek to expand
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matter physics, and quantum computing. You will dive deep into the realms of theoretical and computational physics with a focus on the development of algorithms and large-scale numerical simulations. Your
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engineering, or a related discipline Experience in mathematical modelling and numerical methods for ordinary and partial differential equations Strong interest in working in a cross-disciplinary, collaborative
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systems. The PhD project will utilize a variety of molecular tools to analyze and manipulate biofilms. A particular focus will be on the use of transcriptomics, genetics, and microscopy methods
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systems. The PhD project will utilize a variety of molecular tools to analyze and manipulate biofilms. A particular focus will be on the use of transcriptomics, genetics, and microscopy methods
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transfer. We use complementary approaches including cryo-EM, biophysical methods and in vivo functional assays to study the protein machineries involved in this process. Join our young, ambitious, and