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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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research with the aim of understanding molecular processes in prokaryotic cells and/or interactions between cells. Subject Prokaryotic microbiology with focus on molecular processes Subject description
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methods are developed in parallel, the postdoc will develop systems and services that make biological data accessible to AI and computational tools, collaborating closely with the Human Protein Atlas (HPA
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in several different projects in parallel. You can take initiatives, be creative, have a service minded attitude towards collaborative facility work, in order to help infrastructure users and research
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workflows. •Operation and monitoring of LC-MS/MS and LC-HRMS instrumentation. •Routine quality control and assessment of analytical performance. •Documentation of laboratory work and analytical results
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multi-hour process that remains largely uncharacterized in biophysical terms. Using in-vivo-mimetic systems, this project will investigate how these parasites find, penetrate, migrate through, and exit
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on studying genetic variation and determining how different variants co-occur on the same paternal or maternal haplotype, a process known as haplotype phasing. We have previously developed methods for haplotype
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science (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
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qualifications Publications at top machine learning or computer vision conferences (NeurIPS, ICML, ICLR, CVPR, AISTATS etc.) are highly meriting. Expertise in Bayesian methods, generative models, multimodal models
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analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics. Project description Searches for dark