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Field
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will contribute to the field by: Developing a conversational AI interviewer capable of conducting real-time adaptive interviews. Building an automated candidate ranking model based on interview
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process conditions. Furthermore, this research will focus on the development of a model, allowing for virtual testing and optimisation of the chemical recycling process. This includes potential
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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xenograft (PDX) models. To this end, the candidate will have direct access to cutting-edge mass spectrometry and advanced bioinformatics infrastructure. The project is part of the collaborative research
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, including biosolutions. The institute’s tasks are carried out in interdisciplinary collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is
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spectroscopic techniques, theoretical modelling, and precise synthetic control – have significantly improved the activity, selectivity, and stability of CO₂-to-alcohol conversion. Key catalyst systems include Cu
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expertise in employing synthetic microbial communities, gnotobiotic mouse infection models as well as in molecular microbiology of gut microbiota and enteric pathogens. The TUM School of Life Sciences
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plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: 1
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), mouse models, fluorescence microscopy (confocal), genomics, epigenetics, cell biology and biochemistry techniques (e.g., RNA/ChIP-seq, mass spectrometry). Successful track record in conducting and driving
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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models