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? No Offer Description About cellumation cellumation is a Bremen-based deep-tech company and spin-off from the Bremen Institute of Production and Logistics (BIBA). We develop the celluveyor – a modular
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. Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, J. Chen, A deep learning-enhanced framework for multiphysics joint inversion, Geophysics, 88(1), K13-K26, 2023. https://doi.org/10.1190/geo2021-0589.1 [3
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an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical engineering, computer science & IT
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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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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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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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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proficiency in Python, R, or MATLAB. Experience with Deep Learning frameworks (PyTorch, TensorFlow) and LLM APIs is an asset. Communication: Fluent English skills, both written and spoken, with a demonstrated
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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