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computational approaches to biological systems. Its core activity is the development of deep learning methods for protein design and optimization, with applications in biology and medicine. - activities: We
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Research Infrastructure? No Offer Description TASKS/ROLE * conducting research under the project Design-ready forward and inverse surrogate modeling of high-frequency structures using deep learning and
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this position: https://www.ucop.edu/academic-personnel-programs/_files/2025-26/policy-covered-october-2025-scales/t1.pdf . The current full-time base salary range for this position is $80,800-$212,000 (9-month
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https://pubmed.ncbi.nlm.nih.gov/36596869/ Research area: Cancer biology Keywords: lymphoma, CLL, lncRNA, microenvironment Funding of the PhD candidate: Part-time salary (min. 0,5 FTE) on EHA grant/AZV
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topic, the work addresses exploration of new concepts and technologies (in particular for reusable launch vehicles), and methodological research which includes MDO, surrogate modelling, deep learning and
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artificial intelligence and/or machine learning, digital health, wearables, etc. Contribute to Epidemiology curriculum development, teach epidemiologic methods as part of the Epidemiology PhD methods sequence
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experimental design. Proficiency with machine vision and deep learning techniques, including image segmentation, landmark placement and metric learning, for the automation of phenotypic analysis of large image
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practices. Attend lab meetings, seminars, and participate in departmental and university activities as appropriate. Other Duties as Assigned 5% Qualifications Core Knowledge and Abilities Deep expertise in
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. The BlueMat project ?Multiscale Operando Super-Resolution Imaging of Water Imbibition in Hierarchically Porous Materials? aims to establish methods for super-resolution imaging using deep learning. You will
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep