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Field
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develop processes for the purification of hydrogen obtained from diol-based carrier molecules and to evaluate possibilities for the use of electrochemical compression processes. In this PostDoc position you
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(Shannon entropy, compressibility, effective complexity and logical depth). The data basis for the analyses are two-fold: first, recent connectome data, i. e. large datasets of all synaptic connections in
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that includes. Job description: We are seeking an information theorist with an academic background in mathematics or bioinformatics and a specialization in information theory (Shannon entropy, compressibility
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performance. The impact of this process will be investigated, considering its impact on the compressibility and transfer properties of the mixtures. Successful candidate will engage in interdisciplinary
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device prototypes. Furthermore, the candidate should have experience in polymer processing techniques, including filament extrusion, compression molding, injection molding, and fused deposition modeling
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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in the field of heat pumps and refrigeration systems Knowledge of English. Specific Requirements Modelling and Simulation of CO2 Refrigeration Systems based on liquid piston compression, validated with
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expectations. Preferred Qualifications Previous experience with turbulence over rough walls, porous media, or complex geometries, as evidenced by work history. Knowledge of compressible flow regimes, including
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of compressible flow regimes, including supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as