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will develop novel machine learning and artificial intelligence (ML/AI) methods for genomics data, especially: large-scale single-cell genomics data, high-definition spatial genomics, digital pathology
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning rules in networks of complex spiking neuron models in the application field of geolocalization: Building
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paradigms centered on human perception. Finally, the recent rise of foundation models and multimodal artificial intelligence opens up new perspectives at the interface between coding and machine learning
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Professor level. The ideal candidate will be at the forefront of research that integrates modern machine learning methods with economic theory and econometric analysis. We are particularly interested in
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skills and experience: Essential criteria PhD or equivalent (or thesis submitted*) in at least one of the following subjects: Computer Science, Machine Learning, Biomedical Engineering, Medical Imaging
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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include: (1) implementing light–matter interaction in CFD via the radiation transport equation and suitable attenuation models; (2) integrating kMC-based surface kinetics through machine-learning surrogate
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The research program involves the study of machine learning