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of precision fermentation or cell culture - Affinity towards technical tasks and bioprocess control - Advantageous: Experience in mathematic modeling, programming, CAD, microfluidic or bioreactor systems
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optimization or discrete algorithms. Profound mathematical modeling and programming skills. Experience with the design and analysis of graph algorithms or multiobjective optimization models is a plus. Very good
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knowledge of energy system modelling or climate modelling Good knowledge of deep learning, PDEs or mathematical/numerical optimization methods Enthusiasm for challenging problems and interdisciplinary
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its rich information content, conventional analysis methods have not yet fully realized its potential. This research project aims to develop a robust AI foundation model based on modern Transformer
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, Mathematical modelling, Biology, Environmental Science, Physical Geography, or related fields. • Specific research in agricultural-environmental disciplines, and particularly with respect to livestock
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analysis (TEA) or an affinity towards these research questions. - Basic knowledge in bioprocess design, bioengineering and/or mathematic modeling - Affinity towards research question in life cycle
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messengers transported by the flow or even the pressure of the fluid itself. In an interdisciplinary team, you will either develop theoretical models of the feedback between flow and network architecture
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the 01.10.2022. Your Responsibilities: You will work at the cutting edge of privacy-preserving deep learning research with a focus on one or more of the following topics: - Optimal model design for differentially
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to familiarize yourself with methods of empirical or model-theoretical research, as well as motivation, commitment, and fun in re-search. Ambitions to pursue an academic career are welcome and will be supported
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deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. We are seeking a researcher to join our team in an ERC project on DNA data storage