Job Description:
Position Description
SocioZK is on the lookout for an innovative and driven AI Research Scientist to join our cutting-edge research team. In this role, you will engage in groundbreaking research and development in artificial intelligence, focusing on advanced machine learning techniques and algorithms. The ideal candidate will possess a strong academic background in AI, deep learning, and data science, with a passion for transforming theoretical research into practical applications.
Responsibilities
- Research & Development: Conduct original research in AI and machine learning, exploring novel algorithms and methodologies that advance the state-of-the-art in the field.
- Algorithm Design: Develop and implement advanced machine learning models and deep learning architectures to solve complex real-world problems.
- Data Analysis: Analyze large datasets to derive meaningful insights and identify trends, utilizing statistical methods and machine learning techniques.
- Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to translate research findings into practical applications and products.
- Publication & Dissemination: Publish research findings in reputable journals and conferences, contributing to the academic community and enhancing SocioZK’s reputation as a leader in AI.
- Mentorship: Provide guidance and mentorship to junior researchers and team members, fostering a collaborative and innovative research environment.
- Innovation: Stay abreast of the latest advancements in AI and machine learning, integrating new techniques and ideas into ongoing projects.
Required Qualifications
- Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent research experience.
- Proven track record of research and publications in top-tier conferences or journals in AI and machine learning.
- Strong programming skills in languages such as Python, R, or C++, with experience using machine learning libraries (e.g., TensorFlow, PyTorch, Keras).
- Solid understanding of machine learning algorithms, deep learning architectures, and statistical modeling techniques.
- Experience with data preprocessing, feature engineering, and model evaluation metrics.
Preferred Qualifications
- Familiarity with natural language processing (NLP) or computer vision (CV) techniques.
- Knowledge of reinforcement learning and generative models.
- Experience working in a collaborative research environment or industry setting.
