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Field
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Requisition Id 15854 Overview: We are seeking a research professional with fundamental knowledge in artificial intelligence (AI) who will focus on developing and applying AI algorithms to signal
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algorithms for resource-efficient hydroponics and evidence-based frameworks for integrating green space exposure for improved student wellbeing. This project will potentially enhance urban food resilience
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conducted to assess the feasibility of prototypes in terms of activity tracking, updating game elements, and visualisation capabilities. The second objective of the thesis is to develop models and algorithms
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on real-life data from the maritime industry. As part of the PhD, you will be following advanced courses to extend your skills, implement and test algorithms, and learn to write scientific papers. As part
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development computational biology bioinformatics genomics and proteomics microbiology and microbiome science ecology evolutionary biology Our concept is supported by the facilities available
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environments (preferred). You possess the ability to conduct independent research and develop novel algorithms. You have strong analytical and problem-solving skills. You have a research-oriented mindset and
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FieldBiological sciences » BiologyEducation LevelPhD or equivalent Skills/Qualifications Specialized training in Evolutionary Biology, particularly in parasite–host interactions Laboratory experience in molecular
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types. Your algorithms will first be evaluated through simulation using real operational datasets, and later deployed and tested at two physical facilities: a kW scale testbed at TU Delft’s Green Village
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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce