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structured SPE data Develop ML models to predict key polymer properties relevant to battery performance Create generative models for the inverse design of novel SPE candidates within the targeted chemical
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focuses on predicting cell type-specific responses to genetic alterations, identifying transcriptional signatures indicative of treatment sensitivity, and predicting the effects of the cell type composition
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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of focus are robotics for mines, construction sites, aerial inspection of aging infrastructure, multi-robotic search and rescue, multi sensorial fusion and multirobot coordination, including multirobot
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spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
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advantageous. Good collaboration skills and a structured working approach are important for the role, and you should be independent with your own drive and interest in the research assignment. Great emphasis
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structures and their dynamics to characterise the function and structure of key reaction intermediate will allow you to truly elucidate the processes of H2 catalysis in [FeFe]-hydrogenases and N2 reduction in
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to express and purify proteins from different bacterial groups. Subsequently, you will compare their structural and functional properties. Your tasks will include protein purification and biochemical analyses
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of Technology offers strong support through a qualified supervisory team and a structured doctoral education program leading to a PhD in Geotechnical Engineering. Active participation in the international
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(DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global