Assistant Professor in Statistics

1. Expertise in Statistical Theory and Methods

1. Foundational Knowledge: In-depth understanding of foundational topics in statistics, including probability theory, statistical inference, regression analysis, hypothesis testing, Bayesian statistics, and multivariate analysis.
2. Advanced Statistical Techniques: Proficiency in advanced statistical methods such as time series analysis, categorical data analysis, machine learning, design of experiments, statistical modeling, and data mining.
3. Statistical Software: Proficiency in statistical software and programming languages such as R, Python, SAS, STATA, MATLAB, and others for data analysis, simulation, and modeling.
4. Mathematical Foundations: Strong background in the mathematical principles that underpin statistical theory, including calculus, linear algebra, real analysis, and measure theory.

2. Teaching and Curriculum Development

1. Course Development and Instruction: Designing and teaching undergraduate and graduate-level courses in statistics, such as introductory statistics, probability theory, regression analysis, statistical programming, and more specialized topics like data science or statistical learning.
2. Active Learning: Implementing various teaching methods, including lectures, hands-on labs, group projects, and case studies, to engage students and enhance their understanding of statistical concepts.
3. Student Assessment: Creating assignments, exams, and projects that assess students' understanding of statistical principles, as well as providing timely and constructive feedback.
4. Mentoring and Advising: Advising undergraduate and graduate students on academic and career matters, helping them with course selections, research opportunities, and preparing for careers in statistics or related fields.
5. Student Engagement: Creating a supportive learning environment that encourages student participation, critical thinking, and collaborative learning.

3. Research and Scholarly Activity

1. Independent Research: Conducting high-quality research in areas of statistical theory, methodology, or application, and making original contributions to the field.
2. Applied and Theoretical Research: Engaging in both theoretical research (e.g., developing new statistical methods or models) and applied research (e.g., applying statistical techniques to solve real-world problems in fields such as medicine, economics, engineering, or social sciences).
3. Collaborative Research: Collaborating with faculty from other disciplines (e.g., data science, biology, economics, engineering) to apply statistical methods in interdisciplinary research projects.
4. Publishing: Publishing research papers in reputable peer-reviewed journals, presenting findings at academic conferences, and contributing to the advancement of statistical knowledge.
5. Research Funding: Writing and submitting grant proposals to secure research funding from government agencies, foundations, and industry partners.

4. Data Analysis and Statistical Consulting

1. Collaborative Data Analysis: Collaborating with other researchers or departments to apply statistical methods in analyzing complex data sets, and assisting in the interpretation of results.
2. Statistical Consulting: Providing statistical consulting services to researchers, organizations, or industry partners in need of expertise for data analysis, experimental design, or statistical modeling.
3. Data Visualization: Developing effective ways to communicate complex statistical results to a non-technical audience through visualizations, reports, and presentations.

5. Supervision of Graduate Students

1. Graduate Research Supervision: Supervising Master's and Ph.D. students in their thesis or dissertation research, guiding them through the development of their research questions, data collection, statistical modeling, and analysis.
2. Dissertation Advising: Providing ongoing mentorship and feedback to graduate students to help them succeed in their academic and professional goals, including preparing them for publishing research and academic job searches.
3. Student Research Projects: Encouraging and mentoring students in independent research projects, fostering a collaborative and supportive research environment.

6. Professional Development and Continuing Education

1. Staying Current with Advances in Statistics: Continuously updating knowledge and skills by reading the latest research papers, attending conferences, and participating in professional development activities related to statistics and data science.
2. Teaching Innovations: Experimenting with innovative teaching methods, such as flipped classrooms, online teaching platforms, or interactive statistical software to enhance student learning.
3. Industry Trends: Staying informed about trends in data science, machine learning, and big data analytics, and incorporating these emerging topics into teaching and research.

• Graduate with a MSc. from a reputable university.
• The applicant must have at least (3) years experience in teaching in an academic institution applying the credit hours system.

ESSENTIAL DUTIES & RESPONSIBILITIES:

1. Develop and deliver courses to students in specified discipline areas of study, considering and aiming to achieve the fundamental standards of the University.
2. Evaluate and monitor individual student progress and provide feedback to sustain student success.
3. Actively seek out methods, procedures and resources to best achieve course objectives.
4. Collaborates and supports colleagues regarding research interests and co-curricular activities.
5. Perform miscellaneous job-related duties as assigned.

Skills Required:

• Excellent oral and written communication skills in English (Arabic is a plus).
• Excellent in developing and delivering presentation.
• Excellent in creating, composing and editing written materials.
• Strong interpersonal and organizational skills.
• Knowledge of computerized student information systems.
• Program planning and implementation skills.

Note:

• Only short-listed candidates will be contacted.