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from Okinawa, Japan, to investigate how species interactions and network structure shape response diversity under environmental change. The project will develop and apply AI and machine-learning
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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processing and machine learning techniques; • Integration of the developed system into a microfluidic platform and validation using laboratory samples of known species and, whenever possible, real
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candidate will join an interdisciplinary cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and
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training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication
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automatic sentiment and emotion analysis; iii. Machine Learning and Deep Learning applied to textual data analysis; iv. Development and evaluation of intelligent systems, namely conversational
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, Computing, Data Science, or Machine Learning Desirable A relevant Professional Body Membership as appropriate to the subject area HE teaching qualification (or equivalent experience) Lecturer
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doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based