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Field
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of high value impact activity in knowledge transfer and enterprise that is of manifest benefit to CHASM and the University and: (a) makes a significant contribution to policy development at national and
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data (sequences and associated properties), adapting existing generative architectures (e.g. VAEs and diffusion-based models when appropriate) to the peptide domain, and defining suitable representations
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proficiency – 20%; - Criterion 4: Interest in conducting research and knowledge in the fields of Artificial Intelligence, Data Analysis and Transformation, and Spatio-temporal Data Representation – 40%; VII.III
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representations of knowledge. via Unsplash Qualifications Required qualifications: Master’s degree in humanities or social sciences, such as cultural or linguistics studies, museology, anthropology, history
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well as for the international representation of German interests. Approximately 10,000 people plus approx. 550 visiting scientists are employed in DLR’s 55 institutes and facilities at 30 locations in Germany. Who can apply
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of publications in relevant domains (informatics, machine learning, AI, knowledge representation) Experience with multi-site data harmonization or federated data environments Special Instructions Priority
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behavioural, fMRI and MEG studies Collect behavioural, fMRI and MEG data Undertake computational model-based and representational similarity analyses of behavioural, fMRI and MEG data Write reports and
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representation in natural and artificial intelligence systems in concert. Artificial intelligence has become a technology of great importance to science and education and is interdisciplinary in its nature. INTED
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Life and Health Sciences Research Institute (ICVS), from the School of Medicine (EM) of the University of Minho | Portugal | 8 days ago
call for applications is now open for the attribution of 1 (one) Research Fellowship for Master Enrolled in a Non-Academic Degree Course, within the scope of the R&D project “Mapping neural representation
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, and clinical safety datasets Implement graph-based retrieval-augmented generation (RAG) methods to enhance knowledge extraction and information synthesis Develop cross-pathway analytical methods using