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consequences of keel bone deviations: What impact do these have on hen behaviour and wellbeing? high-tech welfare assessment: Help develop a non-invasive computer vision method to track and analyze how hens move
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and
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: Help develop a non-invasive computer vision method to track and analyze how hens move in 3D space. You will gain hands-on experience in behavioural studies, animal welfare science, and innovative data
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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various international initiatives and engaged with different stakeholders. The candidate is expected to primarily but not exclusively deploy qualitative IS methods, with specific focus on case study
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in another language, if there is no Portuguese or English version of them. Whenever the originals of the documents referred to in III.2.3. are produced in a different language, a translation document
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characterizing defects such as dislocations Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities
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(subproject A1): This project aims to enhance smartphone-based survey methods as one component of future multi-method travel surveys, to achieve representative samples and travel estimates, to collect
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted