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consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will
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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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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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) programme in Artificial Intelligence Engineering. You will be responsible for teaching courses in topics such as machine learning, deep learning, MLOps, image processing, and computer vision, and you will
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data. The project is funded
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span courses from bachelor to master level and will typically cover topics such as machine learning, deep learning, computer vision and programming. You will supervise student projects and theses at both
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Assistant Professor in statistics for the development of privacy-enhancing techniques in health care
Programme, “Synthetic health data: ethical development and deployment via deep learning approaches (SE3D)” which is a collaboration between Head of Center and Professor Martin Bøgsted, Center for Clinical
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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