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University/NL. The successful candidates commit to actively participate in networking including regular research visits to the partner laboratories. Requirements: university degree in chemistry or physics and
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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networks using common Python-ML libraries such as PyTorch preferably also background knowledge in computational mechanics and applied mathematics highly proficient in spoken and written English We offer you
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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to learning opportunities, local research networks, and access and facilities. Candidate profile: Applications are invited from outstanding graduates who meet the following criteria: BSc, including synthetic
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international network. This project is offered as part of a Hans Fischer Senior fellowship through the TUM Institute for Advanced Studies (https://www.ias.tum.de/ias/start/ ) and will be co-supervised by
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of organic and inorganic molecules encapsulated inside 2D materials. As a PhD student, you will become part of an excellent scientific network and benefit from our structured, interdisciplinary graduate
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seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent