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learning methods for protein design Design of enzymes using computational models Identification and optimization of plastic-degrading enzymes Experimental expression, purification, and characterization
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: Development and application of AI and machine learning methods for protein design Design of enzymes using computational models Identification and optimization of plastic-degrading enzymes Experimental
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include an exciting work in terms of hardware/software use and upgrade of additive manufacturing machines to achieve optimal processing, which could be especially empowered by monitoring and computer
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on methodological development in cryo-electron microscopy (cryo-EM), particularly in image reconstruction and 3D volumetric analysis of macromolecular structures. Rather than aiming to incrementally optimize existing
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to control the electronic properties of organic semiconductors through chemical doping. The work will combine molecular and materials design with advanced structural, spectroscopic, electrical, and
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from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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planar optical waveguide structure. The current project focuses on advancing a surface-sensitive, label-free waveguide scattering microscopy (WGSM) platform for nanoparticle characterization in advanced
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optical waveguide structure. The current project focuses on advancing a surface-sensitive, label-free waveguide scattering microscopy (WGSM) platform for nanoparticle characterization in advanced therapies
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provide insights into where energy expenditure is highest and where efficiency improvements can have the greatest impact. By doing so, this work aims to contribute to a better understanding and optimization
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for an expert in mass-spectrometry based proteomics who will work alongside our biochemists and microbiologists on detecting and optimizing casein PTMs . Who we are looking for The following requirements