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). This thesis aims to investigate how to design argument mining systems (detection, classification, and extraction of argumentative relationships in texts) that minimize biases introduced by training data
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EO data and advanced text-mining techniques to characterize biophysical and hydrological dynamics relevant to water resilience. The monitoring strategy combines biogeophysical, hydrological, and socio
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microbiology and structural biology; experimental design and execution; scientific literature research and data mining; molecular biology/genetic engineering (cloning, subcloning, mutagenesis, construction
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. Preferred: background and/or experience in microfluidics and image processing. Strong interpersonal skills, teamwork, scientific curiosity, and ability to synthesize information. Where to apply Website https