Postdoctoral Researcher in Artificial Intelligence

Join to apply for the Postdoctoral Researcher in Artificial Intelligence role at Tulane University

Join to apply for the Postdoctoral Researcher in Artificial Intelligence role at Tulane University

Description

Prof. Ibrahim Demir at the ByWater Institute, Tulane University is seeking a highly motivated Postdoctoral Researcher specializing in next-generation artificial intelligence (AI) methodologies. The successful candidate will be at the forefront of exploring cutting-edge techniques—such as multimodal multi-task transformer-based architectures, large-scale generative modeling, graph neural networks (GNNs), and reinforcement learning frameworks—for applications spanning environmental monitoring, decision support, and educational innovation. Responsibilities include developing and fine-tuning advanced AI/ML models, designing experiments to evaluate model robustness and bias, contributing to open-source libraries, and pursuing novel research directions that leverage interdisciplinary collaborations.

Additionally, the postdoctoral researcher will play a pivotal role in securing competitive funding from agencies such as NSF and NIH, mentoring graduate students, disseminating findings in high-impact journals and conferences, and engaging with diverse stakeholders to translate AI breakthroughs into real-world impact.

Description

Prof. Ibrahim Demir at the ByWater Institute, Tulane University is seeking a highly motivated Postdoctoral Researcher specializing in next-generation artificial intelligence (AI) methodologies. The successful candidate will be at the forefront of exploring cutting-edge techniques—such as multimodal multi-task transformer-based architectures, large-scale generative modeling, graph neural networks (GNNs), and reinforcement learning frameworks—for applications spanning environmental monitoring, decision support, and educational innovation. Responsibilities include developing and fine-tuning advanced AI/ML models, designing experiments to evaluate model robustness and bias, contributing to open-source libraries, and pursuing novel research directions that leverage interdisciplinary collaborations.

Additionally, the postdoctoral researcher will play a pivotal role in securing competitive funding from agencies such as NSF and NIH, mentoring graduate students, disseminating findings in high-impact journals and conferences, and engaging with diverse stakeholders to translate AI breakthroughs into real-world impact.

Qualifications

Applicants must have a Ph.D. in computer science, electrical/computer engineering, or a closely related field, with a focus on state-of-the-art machine learning and AI methods. Additionally, the ideal candidate will have:

• Demonstrable expertise in developing and training large-scale transformer-based models (e.g., GPT, BERT, ViTs) for multimodal (text, image, video, sensor data) and multi-task learning.
• Proficiency in advanced deep learning frameworks (Tensor Flow, PyTorch) for GPU-based, large-batch model training, including experience with distributed and parallel computing (e.g., multi-GPU or HPC cluster environments).
• Familiarity with specialized AI domains such as GNNs, reinforcement learning (especially policy optimization or RLHF), and model fusion methodologies (e.g., cross-modal attention, late fusion, or hierarchical fusion strategies).
• Hands-on experience with large-scale data pipelines, hyperparameter optimization, data augmentation, and model interpretability/visualization tools.
• Working knowledge of high-level Dev Ops practices (Docker, Kubernetes, CI/CD) and cloud infrastructure (AWS, Azure, or others) for production deployment of AI systems.
• A strong publication record, preferably in top-tier AI/ML venues (e.g., NeurIPS, ICML, ICLR, CVPR, AAAI) and relevant journals.
• Emerging track record in leading grant proposals, project management, or collaborative research initiatives, with strong interpersonal and mentoring skills.
• Enthusiasm for interdisciplinary and applied research, with the capacity to bridge AI advancements to different domains.