12 parallel-computing-numerical-methods-"Multiple" Postgraduate positions at Forschungszentrum Jülich
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding
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, method-driven theory to application-driven research. Please find more information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your tasks in detail: Review existing literature, collect
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related field Strong background in numerical methods and machine learning Proficiency in at least one programming language (Python, Julia, C++, …) Good analytical skills Good organizational skills and
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. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and
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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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Your Job: The PhD position is offered in the context of the HDS-LEE graduate school. We are looking for a highly motivated PhD candidate to join our world-leading research program in Earth System
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Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset Strong analytical skills with a solid
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about state-of-the-art methods in machine learning, reinforcement learning and computer vision for the life sciences Your Profile: Excellent Master’s degree in engineering, computer
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computer vision methods and their applications Your Profile: Excellent Master’s degree in engineering, computer science or mathematics (or a related field), with a focus on computer vision, image processing