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Field
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-generation AI models of gene regulation and disease progression. Our work is redefining Parkinson’s disease biology and enabling translational breakthroughs. The role Develop deep learning models across genome
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diseases, Genome Biology, 2024 S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 2023 S. Li et
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evaluate innovative methods based on generative models and Vision-Language Models. Design, implement, and validate deep learning approaches for vision applications. Publish research results in leading
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@emploi.beetween.com Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills/Qualifications Expected skills: Hold a Ph.D. in Deep Learning, Statistics, or a related field. Solid experience in
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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable
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conferences (e.g., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding
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, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer
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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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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view