Postdoctoral Researcher in Artificial Intelligence

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.

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.

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