Organisation/Company: Universidad Carlos III de Madrid
Department: Signal Theory and Communications
Research Field: Computer science » Other
Researcher Profile: Recognised Researcher (R2)
Positions: Postdoc Positions
Country: Spain
Application Deadline: 30 Nov 2024 - 12:00 (Africa/Abidjan)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 37.5
Offer Starting Date: 1 Feb 2025
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Reference Number: Becas Leonardo a Investigadores y Creadores Culturales 2024
Is the Job related to staff position within a Research Infrastructure? No
Offer Description Job Offer: Post-Doctoral Research Position in AI at University Carlos III of Madrid
Position Title: Post-Doctoral Researcher in Generative AI and Machine Learning
Location: University Carlos III of Madrid, Spain
Duration: 1 year (with the possibility of extension for an additional year upon mutual agreement)
Salary: 34,000€ gross per year
Project Title: THAI: Towards Humble and Discoverable AI
Funding: 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.
Participate in outreach and dissemination activities, including presentations at conferences and workshops.
Requirements:
A Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
Strong background in generative models, deep learning, and probabilistic modeling.
Experience with adversarial training, uncertainty quantification, and model robustness is highly desirable.
Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow or PyTorch.
Excellent communication skills and a strong track record of research publications.
Ability to work independently and as part of a multidisciplinary research team.
Application Process:
Interested candidates should submit the following documents:
A detailed CV, including a list of publications.
A cover letter outlining their research experience and how it aligns with the THAI project.
Contact information for at least two academic references.
For more information about the position or the project, please get in touch with us.
University Carlos III of Madrid is an equal opportunity employer. We encourage applications from candidates of all backgrounds and are committed to fostering an inclusive environment.
Where to apply E-mail: ******
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