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the realization of 1:1 full-scale prototypes Assist in robotic fabrication and digital production workflows Contribute to material testing, system optimization, and physical prototyping Support
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-cell data) using existing pipelines Demonstrated ability to enhance, optimize, and extend existing computational pipelines, as well as design and implement new workflows Experience working with large and
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viruses (AAVs) are the leading platform for gene therapy, yet their clinical potential is often capped by sub-optimal tissue tropism, pre-existing human immunity, and low manufacturing yields. This project
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models to diverse energy systems and research questions. The ESA group is headed by Prof. Dr. Russell McKenna and generally focuses on the optimization and assessment of complex systems and infrastructures
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the optimized intervention that resulted from the digital-twin experiments indeed also improved human learning. The group now aims to examine how large language models can serve as digital twins for human
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of machine learning and high-performance computing, tackling complex, open-ended challenges to deliver scalable solutions. You will design and optimize a software-defined infrastructure that enables cutting
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species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large datasets that include over 110,000 tree
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molecular biology, microbiology, and cell biology. Job description Design, implement, optimize, and maintain automated laboratory workflows for life science applications Provide expert support for laboratory
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interdisciplinary team of researchers with expertise in thermodynamics, process design, energy system optimization, and life cycle assessment, working collaboratively with research and industry partners. You will
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limiting factor to further enhance or optimize viral tropism. In this collaborative project, we explore new ways to adjust this equilibrium so the on/off rates of viral vectors brought in proximity to target