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Field
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the laboratory of Dionéia Araldi, MS.c., Ph.D., in the Department of Neuroscience and Pharmacology at the University of Iowa. The Araldi lab investigates signaling pathways that drive the transition
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, societal, and technological aspects of the environment shape this process. Working across disciplines such as psychology, neuroscience, computer science, economics, and sociology, we do research within a
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Do you have a strong affinity for innovative mass spectrometry and delivering a positive impact on global food safety? Do you take joy in creative processes and are you driven by curiosity? Are you
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. Demonstrable experience with data processing, signal analysis and programming (e.g. Python/Matlab). Ability to work independently as well as in a multi-disciplinary team. Fluent in English (oral and written
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the dosage of a liquid must be controlled, such as food processing, drug production, microchips and solar panels. In this project, a commercialised flow sensor that works with ultrasound is further developed
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this scope will not be considered. Topics within the three focus areas include: Entrepreneurial actors and processes, including entrepreneurs’ goals, cognition, emotions, identity, well-being, decision-making
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that transduce environmental and developmental signals; recent relevant papers (2023-2024): https://doi.org/10.1093/plcell/koad288 https://doi.org/10.1371/journal.pbio.3002305 https://doi.org/10.15252/embj
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. This highly innovative project aims to develop a fully AI driven digital twin that enables real-time optimization and control of fermentation processes. The candidate will develop the digital twin for microbial
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of the soil biota in ecosystem processes at a range of scales; from life in a single soil ped to the role of soil biota in climate change at a global scale. We have state-of-the-art laboratories which
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validation of safety signals across observational data networks. Key research areas may include: Developing and evaluating AI-solutions in line with responsible AI principles (e.g. robustness, fairness