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setups and instrumentation, in particular in-situ computer tomography Proficiency in one or more programming languages relevant to the task (e.g., Python, Matlab, LabVIEW, C++) Experience in programming
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, TensorFlow, Pandas), ideally combined with knowledge of data visualization or statistical analysis Knowledge of software development (e.g., Python, Matlab, Simapro), especially in combination with experience
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, computational data analysis and microbial evolution, with a solid understanding of quantitative statistics and programming (Python, R, MATLAB, etc.). Additional expertise in ancient DNA, (bio)archaeology
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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, cloning etc.) Preferably knowledge in the characterization of microbial rhodopsins Skills in data analysis with Python Excellent organizational, interpersonal and communication skills The ability to work in
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., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted in the Department of Microbiome Dynamics of Prof. Gianni Panagiotou of the Leibniz-HKI in
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and
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development knowledge of the state-of-the-art in climate physics and Earth system modeling and data analysis techniques programming skills e.g. in C, BASH, Python, Latex software user knowledge: MS
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
, Python, Unix/Linux command-line tools, and workflow automation Profound understanding of the relevant metrics and methods to analyze microbial communities and functions and their spatio-temporal patterns