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Field
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. You will review the ability of existing monitoring strategies based on remote sensing and spatial statistics to evaluate and communicate the effectiveness of measures on biodiversity, climate and
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participants who may require reasonable adjustments are encouraged to contact the supervisor to discuss these aspects of the project. Desirable MSc in Clinical Exercise Physiology (or other relevant discipline
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Criteria A MSc degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field; Strong knowledge of and experienced with statistics, machine learning, and stochastic
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physiology, etc. Experience of working with clinical populations and collecting human data Proficient in Microsoft Office, specifically Microsoft Excel Desirable MSc in Clinical Exercise Physiology (or other
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a very good MSc degree (or equivalent) in a social and behavioral science discipline (e.g., Psychology, Cognitive Sciences, Decision Sciences) or a computer science discipline (e.g., Computational
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degree from a field that is relevant for the project, for instance a MSc in ecology, geography, biology, or environmental sciences. Motivation to develop the following skills is highly valued: skills in
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and/or Python. Experience in, and aptitude for, complex statistical modelling (inc. mixed effects regression models and/or Bayesian statistics). Excellent written and spoken English. Desirable (traits
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of these other institutions and will be co-supervised by Prof. Gianalberto Losapio (University of Milan). Your profile The candidate must hold a MSc degree in Environmental Sciences, Biology, Ecology, Conservation
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leveraged for this work i.e., unsupervised learning, data clustering including statistical modelling and estimation, noise and heterogeneous information fusion, and multiple-source localization and tracking
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in Computer Science or Artificial Intelligence with a strong mathematical background, or an MSc degree in (Applied) Mathematics or Statistics, or an MSc degree in a related field with a comparable