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Field
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techniques for food safety monitoring, and evaluate the analytical performance aspects of different types of advanced instrumentation. Additionally, you will learn about a wide range of activities related
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, including differences related to sex and ethnicity. The resulting methodologies and datasets will support many of the other CDTnet projects by providing population-scale digital twin frameworks that can be
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learning, and computational modelling. The doctoral candidate will investigate how pressure differences can be calculated directly from magnetic resonance imaging (MRI) and ultrasound data, reducing the need
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, computer science, physics, data science or a closely related discipline. • Experience in MRI image analysis and scientific programming, preferably using Python and modern computational libraries. • Experience
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different nationalities, with whom you will work together in an informal atmosphere. This is your team You will be part of the research group Tropical Botany (Dr. Angelica Cibrian Jaramillo, Dr. Paola De Lima
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relevant discipline Experience in fish welfare and behaviour Experience in image or video analysis, including using AI/computer vision tools Experience with R or Python for data analysis Experience designing
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individually, make a real difference. The role We are recruiting a postdoctoral researcher for project PROTEQT (QuantERA) (Fixed term contract upto June 2027). The successful candidate will lead the theoretical
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Networks (GNNs), Convolutional Neural Networks (CNNs), and Transformers. These resources will be made available to researchers across different scientific domains. Ultimately, this initiative will empower
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expertise developing, validating and deploying advanced statistical and machine learning models using Python, R, or similar programming languages in a research environment. Experience working with melanoma
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health research, or related fields. Substantial Experience in R, python or other relevant computational languages. Experience with single-cell RNA sequencing and/or ATAC-seq. Demonstrated experience