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SD-26065 -POST-DOC IN METHOD DEVELOPMENT FOR HIGH RESOLUTION CHARACTERIZATION OF NOVEL SAFE AND S...
to fulfil their personal and professional ambitions · Gender-friendly environment with multiple actions to attract, develop and retain women in science · 32 days’ paid annual leave, 11 public
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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, chemical fixation and resin embedding, FIB milling, cryo preparation) Experience in developing and using correlative workflows, data processing and data visualisation, including methodologies and algorithms
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aims to develop a new generation of mobile robots capable of withstanding shocks, absorbing impacts, and recovering from collisions in complex, unstructured settings. The concept of resilience here
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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large-scale multimodal neuroimaging dataset, brain
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scientific data Major Duties/Responsibilities: Design and implement advanced AI architectures and workflows for imaging and spatiotemporal data. Develop efficient and scalable training algorithms
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principle, active inference, triple equivalence, and related theories 2.Develop neuromorphic (biomimetic) learning algorithms for next-generation artificial intelligence 3.Develop a universal generative model
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connect to our group’s work and how this position supports their career development goals. Possible research topics include (but are not limited to): Optimization algorithms for machine learning (stochastic
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration