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Field
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related field; Strong publication record in relevant venues; Solid programming skills in Python and a modern deep-learning framework Excellent communication skills in spoken and written English; Self
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. - Strong expertise in multiscale/multiphysics modeling relevant to catalysis, and experience with machine learning models. - Deep understanding of reaction kinetics, thermodynamics, and structure-reactivity
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subject The DEEPICE project aims to develop an automated workflow combining glacial geomorphology, remote sensing, DEM analysis and deep learning to detect and analyse subglacial bedforms at large scale
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characterising chronic diseases and disease patterns from electronic health record (EHR) data through the development of advanced deep learning methodologies based on state-of-the-art foundation models. You will
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for cardiometabolic disease with associated neurodegenerative conditions. It aims to develop novel deep learning algorithms, audio and vision transformers, and hybrid attention mechanisms, to detect
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%) Qualifications Required Qualifications: •PhD in biomedical engineering, psychiatry, neuroscience, physiology •Strong publication history and evidence of skill in MRI data acquisition and analysis •Ability
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the employee and the PI (15%). Qualifications Required qualifications A PhD in isotope geochemistry is required At least 5 years of hands-on experience in sample preparation in trace-metal-free clean labs
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calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed before the position begins. Additional information
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as natural experiments and interpretable machine-/deep-learning models. Publish research results in high-quality international journals (at least two peer-reviewed papers are expected). Eligibility
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learning and deep learning techniques to the biological sciences. The ideal candidate will have expertise in artificial intelligence, with a specific focus on deep learning applications in structural biology