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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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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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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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experience implementing and evaluating machine learning models for protein sequences. Strong analytical skills and an interest in interdisciplinary research. Proficient communication skills and ability to work
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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computational methods as the necessary foundation to venturing and expanding the field through modern approaches in machine learning and artificial intelligence. The appointment is hosted by the Department
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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Perception Lab. Your competencies You have a strong background in computer vision and artificial intelligence, documented by a relevant PhD degree in, for example, computer science, machine learning, computer
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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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The Department of Electrical and Computer Engineering (ECE) at Aarhus University (AU) invites applications for a tenure-track position as Assistant Professor in Electronics. We seek a talented and