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of individual atoms. The development of this technology is crucial for the next generation of quantum device fabrication techniques and has great commercial interest and potential for patenting. The skills and
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methods including machine learning methods to integrate genomics, transcriptomics and epigenomics data set to uncover genomic-epigenomic interactions and cancer evolution trajectories. The tasks will
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. These transporters function as molecular machines, harnessing ion gradients to drive substrate uptake, and display features of enzymes, receptors, and ion channels. Grounded in biophysical pharmacology, our lab
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, biochemistry, deep sequencing, proteomics, metabolomics, genetics, phage engineering etc. Besides, academic exchanges to China (and/or Switzerland, Germany) are possible depending on the project progress. Who
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used will be Density Functional Theory, statistics, machine-learning and dynamics. Collaboration with members of other research groups at UCPH and abroad is required. Who are we looking for? We
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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points Participating in active research environments including a stay at another research team Obtain experience with teaching or other types of dissemination related to your PhD project Teach and
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scholarship, to contribute to the project’s substantial collective prosopographical work (among other things with the aim of creating a new version of VisuaLeague ), and to learn and grow as a scholar together