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communities as a model system. Main duties and responsibilities Anaerobic digestion communities are among the best-characterized and most ecologically important microbial ecosystems. Composed of diverse
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computational modeling, mathematical modeling, or experimental psychology. Profile We thrive on interdisciplinary collaboration. You should hold a PhD in musicology, computer science, cognitive science
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. Prior experience with animal models, 3D culture systems and/or computational analyses will be an advantage. The salary will be according to EPFL standards. Informations Formal applications should be
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through proprioception. We develop widely-used open-source tools (e.g., DeepLabCut), train biomechanically realistic embodied agents (e.g., MuscleMimic, Kinesis, Arnold), and build AI-based models
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of an optoelectronic implant for bladder dysfunction in rodent models Collect and analyse electrophysiological and behavioural data Histologically evaluate tissue following chronic implantation Work independently and as
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the field of sustainable construction. The research integrates dynamic material value-chain analyses, ownership models, technical performance assessment, and design-for-disassembly strategies to support the
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investigate simplified and analytically tractable models aimed at identifying general principles governing non-equilibrium chemical reaction networks. The position is primarily research-oriented. Contributions
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Modelling (BIM), Geographic Information Systems (GIS) or computer programming is a plus. A master's degree or professional experience in architecture is preferred but not required. The employment rate varies
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/2603.06760 . A potential target application is the design of model gliders to compete in FAI competitions such as https://www.fai.org/page/f5-electric-flight . Profile Potential candidates are expected to have
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Experience with machine learning and/or statistical modeling applied to biological data Proven expertise in single-cell data analysis (scRNA-seq and/or scATAC-seq) Interest or experience in multi-modal data