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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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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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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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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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scienceYears of Research ExperienceNone Research FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria Degree : PhD in computer science, machine learning, or computational
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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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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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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 This post-doctorate is part of the ANR DEEP-SENS (Demystifying
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the creation of high-precision digital twins. Activity 1: Integration of Photometric Stereo in Meshroom - Implement processing nodes for normal field and intrinsic color estimation. - Integrate deep learning