Post-Doctoral Research Position In Ai At University Carlos Iii Of Madrid

hace 2 días


Madrid, España Universidad Carlos Iii De Madrid A tiempo completo

.Organisation/Company: Universidad Carlos III de MadridDepartment: Signal Theory and CommunicationsResearch Field: Computer science » OtherResearcher Profile: Recognised Researcher (R2)Positions: Postdoc PositionsCountry: SpainApplication Deadline: 30 Nov 2024 - 12:00 (Africa/Abidjan)Type of Contract: TemporaryJob Status: Full-timeHours Per Week: 37.5Offer Starting Date: 1 Feb 2025Is the job funded through the EU Research Framework Programme? Not funded by a EU programmeReference Number: Becas Leonardo a Investigadores y Creadores Culturales 2024Is the Job related to staff position within a Research Infrastructure? NoOffer Description Job Offer: Post-Doctoral Research Position in AI at University Carlos III of MadridPosition Title: Post-Doctoral Researcher in Generative AI and Machine LearningLocation: University Carlos III of Madrid, SpainDuration: 1 year (with the possibility of extension for an additional year upon mutual agreement)Salary: 34,000€ gross per yearProject Title: THAI: Towards Humble and Discoverable AIFunding: This position is funded by the prestigious "Becas Leonardo a Investigadores y Creadores Culturales 2024" from Fundación BBVA, which grants only 4% of the proposals submitted. The PI of the project is Pablo M. Olmos .About the Project:The THAI project seeks to address critical challenges in the field of generative AI, particularly focusing on issues of overconfidence in AI systems, the robustness of AI-generated outputs, and the detection of synthetic data. As AI models like Large Language Models (LLMs) and Diffusion Models become increasingly integrated into various sectors, their overconfidence can lead to significant risks, including generating plausible yet incorrect information, security vulnerabilities, and the erosion of trust in AI-generated content.The project will explore novel methods to improve AI systems through three main objectives:Uncertainty Calibration through Constrained Latent Encoding: Develop hierarchical generative models that utilize discrete latent spaces to improve calibration and reduce the risk of overconfidence in AI predictions.Robust Generative Decoding: Implement advanced adversarial training techniques and stochastic regularization layers to enhance the resilience of generative decoders, making them less vulnerable to adversarial attacks.Discovering Traces of Synthetic Data: Identify unique markers in AI-generated content to differentiate between human-generated and AI-generated data.Responsibilities:Conduct cutting-edge research on deep generative models, focusing on improving the reliability and trustworthiness of AI systems.Collaborate closely with the Principal Investigator and other team members to design, implement, and test novel AI models and algorithms.Lead the development of experimental frameworks for model training and evaluation.Contribute to the dissemination of research findings through publications in top-tier conferences and journals



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