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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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of ultra-diffuse galaxies; extragalactic tidal phenomena; intra-cluster diffuse light; and galactic cirrus clouds. These measurements will also be compared with other deep-field photometric data (with
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: The postdoctoral researcher may be required to supervise the work of interns or PhD students - Sampling and measurements in the field, - Acquisition and processing of microbial ecology data (genomics and
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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description In the framework of the PEPR FairCarboN project Deep-C, we offer a five
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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transmutation of long-lived radioactive elements is a research area that complements deep geological disposal. Thus, the recycling and transmutation of minor actinides (MA) would reduce their radiotoxicity and