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GravNet and the Theoretical Physics Group This position will be funded by the ERC-2024-SyG GravNet 10116721 (GravNet: A Global Network for the Search for High Frequency Gravitational Waves). GravNet will be
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? No Offer Description The researcher will have to carry out theoretical and computational studies aimed at identifying how cells represent their environment in their molecular networks. The work will
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candidates will be embedded in a large network of international collaborations. The successful candidate will receive full support and help from the group leader in applying for independent funding if wished
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. Familiarity with statistical modelling, machine learning and deep-learning Additional information: We offer: 🌐The opportunity to work with our state-of-the-art HPC infrastructure and to join a vibrant network
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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into the network led in 2017 to the detection of a neutron star binary merger that could be followed in electromagnetic signals, representing the beginning of multi-messenger astronomy. At the moment, LIGO and Virgo
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into the network led in 2017 to the detection of a neutron star binary merger that could be followed in electromagnetic signals, representing the beginning of multi-messenger astronomy. At the moment, LIGO and Virgo
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surrogates of electronic Hamiltonians. The postdoctoral researcher will develop graph neural networks based on the MACE architecture to predict Hamiltonian elements for 2D materials and van der Waals
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networked companies) in the energy and automotive sectors, as well as labour relations in Europe during the 20th century. - Knowledge of European industrial and business history during the 20^(th) century
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encompasses various in silico modeling techniques to cover the multiple spatial scales, i.e. from molecular detailed to multicellular high-level regulatory networks, up to tissue level finite element models