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and assembloid co-culture system to study disease. By integrating single-cell multiomics, spatial transcriptomics, long-read sequencing, and high-throughput functional imaging, we aim to identify
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these intricate immunological processes. Using advanced bioengineering methods and innovative molecular tools, this project aims to: Develop robust 3D skin-on-a-chip models incorporating genetically engineered
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related field, with a focus on computing or data science. Additional requirements are: Significant experience in bioinformatics, genomics, computing, machine-learning, or other data science area
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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged
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research on plasma deposition of functional materials on textiles. Conduct research on plasma-based deposition of functional topcoats for engineered textiles. Develop and optimize plasma deposition processes
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activation deconvoluted with single-molecule precision . Single-molecule analysis of ligand efficacy in β2AR-G-protein activation. Single-molecule FRET imaging of GPCR dimers in living cells Quantifying
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, or other Maxwell solvers. Experience with machine learning algorithms is an advantage but not required. General qualifications Scientific production and research potential at the international level
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System, Computer and Communication Networks, Computer Theory, Machine Learning, Reinforcement Learning；Structural Engineering, Transportation Engineering, Water Resources Engineering & Science, Industrial
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reconstitution systems to study T cell signaling dynamics Cutting-edge CRISPR-Cas9 genome editing tools for targeted manipulation Advanced imaging platforms to visualize protein-protein interactions Primary human
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embedded in the section for Process & Materials Engineering, where the research focus is almost entirely on development of new sustainable solutions, materials and processes for the green transition. AU-BCE