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at Forschungszentrum Jülich, in close collaboration with bioimage analysis partners at Karlsruhe Institute of Technology. Your tasks in detail: Develop and extend deep-learning–based segmentation, classification and
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. The primary objective is to develop computational methods, using deep learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted
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such as the Journal of Investment Management conference. Teaching: Instruct MFE courses focusing on investments, financial markets, data science, deep learning, security valuation, and the numerical
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 5 days ago
programming skills in languages such as Python, C/C++ and CUDA # Familiarity with modern deep learning frameworks like Tensorflow 2.x.x, PyTorch # Mandatory experience with High-Performance Computing (HPC
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microfluidics, nano-electronics, nano-biomaterials, big data, and deep learning. Applicants must hold an M.D., Ph.D., or equivalent degree and have extensive postdoctoral experience, along with a strong
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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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about where a new hire would be placed on the range. To learn more about the benefits of working at UCSF, including total compensation, please visit: https://ucnet.universityofcalifornia.edu/compensation
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to coordinate procedures and teaching resources Part time (0.8FTE), fixed-term (2 years) role based in Launceston About the opportunity Support quality learning and teaching to enhance the student experience and
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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied