A Ph.D. degree in a field related to quantitative analysis, such as physics, mathematics, artificial intelligence, computer science, applied mathematics, electrical engineering, or similar, completed within the last 2 years or within the next 3 months. Strong background in AI algorithms, machine learning, and deep learning. Proficiency with major deep learning packages such as TensorFlow, PyTorch, Keras Proven track record of research excellence, evidenced by publications in reputable conferences and journals. Strong programming skills in languages such as Python, C/C++, and MATLAB . Experience in Bayesian Statistics and Foundational models Experience with developing machine learning models for both server-based embedded solutions. Excellent problem-solving abilities and a collaborative mindset. Strong communication skills, both written and verbal.