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projects and to acquire funding. In education, you will teach in the Biology programmes, develop and coordinate a new course, and supervise research projects of BSc, MSc and PhD students. Where to apply
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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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conducting experiments for training and evaluating deep neural networks Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning Experience in dealing with large medical
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. We are passionate people, serving the greater good. The University of California, one of the largest and most acclaimed institutions of higher learning in the world, is dedicated to excellence in
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solutions and applying them in real-world scenarios. Proficiency with machine learning frameworks and pipelines in SKLearn, Numpy, Pandas, and PyTorch. Proficiency with deep learning frameworks such as
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ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en ) Eligibility requirements
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the Job related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and
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/deep learning with GWAS/sequencing data and other types of omic data, as in Mendelian randomization and TWAS. In addition to new methods development and evaluations, the job responsibilities include
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
athletic programs, Carolina is an ideal place to teach, work and learn. One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and
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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