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AWI - Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research | Bremerhaven, Bremen | Germany | 4 days ago
Infrastructure? No Offer Description PhD Position in "DynaDeep - Biogeochemical Processes in the Dynamic Deep Subsurface of High-Energy Beaches" (m/f/d) AWI - Alfred Wegener Institute Helmholtz Centre for Polar
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of topics is covered, from large-scale data management to data mining and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics
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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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, particularly deep learning and optimization methods Excellent coding skills, particularly in Python and machine learning frameworks (PyTorch or Jax) The ability for creative and analytical thinking across
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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Description We offer a deep immersion in bio-based energy technologies; the candidate will learn and live the translational perspective of designing biomaterials for sustainable energy-related applications
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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(TV-L Brandenburg). Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use dynamics and the economic trade-offs involved. We aim to develop and