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Field
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at RIBES using machine learning and genomics to overcome linkage drag and accelerate crop breeding by design. You will build models, analyse new sequencing data and optimise strategies for combining
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Excited to shape the future of hearing aids? Ready to combine your expertise in Signal Processing, Optimization and Machine learning with Perception and Intelligibility? Job description Good hearing
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electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is to utilise material platforms that exhibit multiwell behaviour
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subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
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Master’s degree in data science research, or a comparable domain. A strong background in machine learning (specifically generative models) and/or symbolic reasoning. Ability to conduct high quality academic
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the electronic structure and to realise artificial matter with exotic electron ground states. Another promising route towards physical implementations of energy-based machine learning and neuromorphic hardware is
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profile: A PhD in AI, preferably at the interface of information retrieval and machine learning; Research background in generative information retrieval, with publications in the leading venues relevant
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community). What we ask of you We are looking for enthusiastic and curious candidates who meet the following profile: A PhD in AI, preferably at the interface of information retrieval and machine learning
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preferably start around 01-12-2025 and requires knowledge of geometric image processing and machine learning. Information As Post-doctoral Researcher within VICI Project "Geometric Learning for Image Analysis
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for this position, the following is required: PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science, climate etc. with a thesis subject relevant to the description