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Field
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they are exceptionally suited for PhD education, for example through relevant work experience and/or peer-reviewed academic work. You must have good knowledge of machine learning and deep learning, with a sound
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation
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considered a strong asset. Experience with Deep Learning and Artificial Intelligence is considered a plus. Excellent proficiency in the English language is required, as well as good communication skills, both
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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) Recognised Researcher (R2) Positions PhD Positions Application Deadline 28 Sep 2026 - 12:00 (Europe/Copenhagen) Country Denmark Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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data science, biomedical engineering, technical medicine, or a related field. You should have strong programming skills (Python, PyTorch), deep learning knowledge (multimodal learning, longitudinal
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University of Science and Technology (NTNU) has a vacant position as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the
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spectral analysis, entropy-based metrics, graph representations of cardiac conduction, and supervised, unsupervised, and deep learning approaches for classification of abnormal electrical activity within