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Science, Materials Science, Engineering, Physics, or a related domain. Proficient in Python; experience or eagerness to learn ML libraries (PyTorch, TensorFlow, scikit-learn), Familiarity with time-series analysis 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 or Libre
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your research at conferences, workshops, and through scientific publications. Your profile: ▪ You hold an MSc degree in physics, engineering, or computer science. ▪ You are very experienced in Python
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system analysis or comparable Initial experience in optimization or statistics is an advantage Initial experience of an object-oriented programming language (e.g. Python, Matlab) and MS Office Analytical
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to submit the dissertation after 3 years and 9 months of research. Desired requirements specific to this project include: experience with a range of relevant computer programming languages such as Python, R
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(preferably in Python) Very good knowledge in German or English Please feel free to apply for the position even if you do not have all the required skills and knowledge. We may be able to teach you missing
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experience with algorithms relevant to computational biology documented programming skills, e.g. in Python and R very good communication and organizational skills with the ability to work to timelines, both
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programming, preferably in Python You have successfully acquired and managed third-party funded projects and have a track record of publications in recognized, peer-reviewed journals You have already taken
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Networks (SNNs) is a core requirement; familiarity with Convolutional Neural Networks (CNNs) is also expected Proven experience with Python frameworks such as Keras, PyTorch, or SNNtorch for neural network
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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