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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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models and transformer-based architectures to construct high-dimensional design spaces. These models are integrated with Deep Reinforcement Learning (DRL) for fine-tuning or end-to-end learning, enabling
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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research
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requires a deep understanding of the solvents involved, such as highly concentrated aqueous or non-aqueous electrolytes. Accurate modeling of these systems relies specifically on the knowledge
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related fields. Additional optional skills and qualifications: Experience in deep learning for medical imaging. Contracting requirements: Presentation of the academic qualifications and/or diplomas
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Classification Title: Assistant/Associate/Full Professor Classification Minimum Requirements: a Ph.D. in computer science or related field Expertise in AI/ML, deep learning Expert proficiency in
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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To find out more about this role, including details of how to apply, please visit quoting reference 7990/1 https://plusportal.perrettlaver.com/VacancyDetail/90f9745c-253c-69a7-4d92-3a1fa2a0e672 Proud of our
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Description The Deep Learning laboratory in the Division of Science, New York University Abu Dhabi, seeks to recruit a Junior Research Scientist to work on Deep Reinforcement Learning (DRL
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics