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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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Nanomaterials that detect protein-structural changes Nano-optical devices for protein-signal sensing AI algorithms for protein structure and dynamics prediction Outstanding Postdoctoral Training Strategy
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sequencing and synthesis to design useful cell behaviors. The scope of this project is to combine multi-gene control technology and computer algorithms to develop a foundational discovery platform for future
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As a fellow you will join our faculty in the Department of Biostatistics, providing statistical support and developing innovative biostatistical methods for research projects at the cutting edge
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, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured light-trapping
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attacks Develop and implement ML algorithms to identify vulnerabilities and predict potential threats in supply chain systems Prepare project deliverables and disseminate results through high-quality
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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is to investigate which antigen specificities are enriched in cell subpopulations, depending on the underlying neurological disease. The project will use high-throughput data to develop and apply
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analysis, data modeling, and algorithm development. Experience with environmental analysis or microplastic research is a plus but not required. Strong analytical and problem-solving skills, ability
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with