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Field
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and do experiments at large facilities. Time-resolved scattering experiments allow us to monitor rapid structural changes. These experiments are complemented by laboratory methods, such as atomic force
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sequencing, genetic modification, or protein biochemistry. You will work with bioinformatic tools and workflows to analyze sequencing results. Data analysis will include the use of statistical methods
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial
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situ or large-scale genome engineering, and data-driven biology. Our Unit is at the forefront of understanding molecular structure, organization and function at a systematic level. We employ a suite of
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technology Qualified support through your scientific colleagues The chance to independently prepare and work on your tasks Flexible working hours as well as a reasonable remuneration A large research campus
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with: A large research campus with green spaces, offering the best possible means for networking with colleagues and pursuing sports alongside work Comprehensive training courses and individual
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-impact questions in genome biology. Our Unit is at the forefront of understanding how molecular information (DNA, RNA, proteins, metabolites) is regulated, integrated, and reshaped within cellular and
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/chemometrics and big data management Proficiency in bioinformatics for linking different -omics datasets Demonstrated experience in writing first author-publications, excellent publication track record in
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Information and Knowledge Engineering: (Co-)Development of domain-specific vocabularies, ontologies and metadata schemas in collaboration with colleagues across the HMC Organization and integration of metadata
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wave circuits (main focus) Generation of low-noise THz signals Research into the large-signal behavior of THz power components Development and use of THz sources and detectors, e. g. in spectroscopy