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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models
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addition to teaching duties, the PhD candidate is expected to conduct research in the field of (deep) machine learning, with applications in either biomedical image understanding (e.g., surgical video analysis in
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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, multilevel analysis). Knowledge in developing predictive and forecasting models in health or environmental research. Skills in machine learning or AI techniques for prediction of complex outcomes. Experience
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an increased interest in adapting and developing the latest machine learning methods for the purpose of malware detection, and preliminary results are encouraging. The specific goals of this project include
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affected by warping, addressing both audio analysis and synthesis tasks. The methodological scope spans stochastic signal processing and machine learning, including hybrid physics‑guided and data‑driven
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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master’s degree with academic qualifications in digital health, data analysis, and/or machine learning applied to health research. Admission to the PhD program requires a 120 ECTS master’s degree, including
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an MD or PhD in Biochemistry, Neuroscience, Microbiology, Immunology, Genetics/Genomics, Biomedical Sciences, Biology, or a related field of study from an accredited institution or a terminal degree in
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-driven biocatalysis and accelerate bioprocess development. DC1: Machine learning-guided multiparametric optimisation of cytochrome P450 monooxygenase PhD enrolment: Technical University of Denmark DC2