96 big-data-machine-learning-phd Postdoctoral positions in Ireland-University-Ranking-2024 in Denmark
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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study design, data analysis, manuscript preparation, presenting findings at international conferences, and mentoring students. Your competencies The ideal candidate has: A PhD in machine learning
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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debates on embodiment, diversity, and educational inequality, and to advancing innovative theoretical and methodological approaches to the study of embodied learning. Further information Applicants
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others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent frameworks). Hands-on experience with data representation and embeddings, ideally applied
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within this field. Your work tasks In this position you will conduct research within Computer Vision and Deep Learning, with a particular focus on the development of an AI-powered framework
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. Your work tasks This Postdoc sits at the intersection of mathematics, statistics, data science, and public health. The goal is to develop new methods that allow researchers to learn from sensitive health