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Field
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meaningful feedback to patients, clinicians and policymakers The PhD will work at the interface of machine learning, deep learning, geospatial AI, causal modelling, and digital health systems. Your Role You
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for enantioselective C-H functionalization chemistry using the latest deep learning tools for protein design. Non-selective photo-chemical methods for C-H heteroarylation have been established using di-aryl ketones as a
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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Professorhip grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a PhD fellow your tasks include: Conduct research under the supervision of senior CNAP staff members
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advertisement Job description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work
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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and
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to plant biosciences where the impact could be huge and as a result exciting opportunities get missed. When we use light to image deep into complex samples there is a common problem that occurs – the light
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | 3 months ago
expose the successful candidate to cutting-edge genome editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid nanoparticles. The successful candidate will also learn
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Functions Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets Modeling plant growth, carbon allocation, stress response (e.g