Tenure-track Assistant and Associate Professorship positions in Data Science and Machine Learning at the Department of Mathem...

The tenure-track Assistant and Associate Professorship positions in Data Science and Machine Learning at the Department of Mathematics require advanced expertise in data science methodologies and machine learning techniques. Candidates should possess a strong foundation in mathematical principles underpinning these fields, including statistics, linear algebra, and optimization. Proficiency in programming languages commonly used in data science, such as Python or R, and experience with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn are essential. The role demands a commitment to conducting high-quality research, demonstrated by a robust publication record or potential for impactful scholarly contributions. Candidates are expected to have experience with data analysis, algorithm development, and possibly large-scale data processing tools. Teaching responsibilities necessitate the ability to convey complex concepts in data science and machine learning to undergraduate and graduate students effectively. Strong communication skills, collaboration in interdisciplinary research, and the capacity to secure research funding are also important. A doctoral degree in mathematics, computer science, statistics, or a closely related field is required, reflecting a high level of education rigor. Overall, the position suits candidates with a blend of theoretical knowledge, practical technical skills, and academic experience in data science and machine learning, aiming to contribute to both research excellence and educational advancement within the department.