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Job description

Providence, RI

The Division of Biology and Medicine at Brown University is seeking a dynamic and experienced Adjunct Faculty member to develop and instruct a new foundational course in graduate level statistics for biology and health during the Spring semester of 2025. This course will serve the learning needs of students across an array of biologically focused graduate programs within the Division. The ideal candidate will bring a strong background in statistics with demonstrable application in biology or health, a commitment to fostering an inclusive learning environment, and experience in successfully developing and leading graduate level statistics course(s) for life science trainees. Experience in the development and use of statistical methodologies to analyze and interpret large sets of biological or health data is favorable. The successful candidate will be passionate about teaching and committed to enhancing the analytical skills of our graduate students.

The appointment will begin on January 6, 2025 with an end date of May 31, 2025. The appointee will be hired on the adjunct professor track at a level commensurate with experience. Using a drafted syllabus as a foundation, the appointee will customize a course in biostatistics accessible to graduate students from across our graduate programs; an approach that permits the candidate to integrate their training and teaching experiences. These programs include: BioMedical Engineering; Biotechnology; Computational Molecular Biology: Ecology, Evolution, and Organismal Biology: Molecular Biology, Cell Biology and Biochemistry; Neuroscience; Pathobiology; and Therapeutic Sciences. The course will be offered in person during Brown University’s spring 2025 semester and is expected to enroll ~60 students. The appointee will consult with faculty members across our graduate programs to ensure course content and pedagogy meets the learning needs of our students. The appointee will have access to teaching assistants for the course, a community of teaching-focused faculty members, and colleagues in the Office of Graduate Studies for administrative support. Applicants should note that as the Division of Biology and Medicine continues to grow its academic portfolio of courses in the fields of biostatistics, big data, and machine learning, future opportunities for long-term teaching track positions may become available within the Division.

Key Responsibilities:

• Course Development & Instruction: Teach a foundational graduate-level course in statistics for biology and health, designed for multidisciplinary PhD students training in the biological and medical sciences. This course will be developed for graduate learners with introductory and intermediate biostatistics knowledge. Using a pre-developed syllabus outline as a foundation, the faculty will further develop materials for the course, deliver lectures, facilitate discussions, emphasize statistical programming skills (e.g., R, SPSS, and SAS), and hands-on data analysis projects. Employ evidence-based pedagogy for biostatistical learning and ensure an inclusive classroom environment.
• Student Support: Provide timely academic guidance and support to students, including holding student hours and offering feedback on assessments. Commit to fostering a healthy learning environment with inclusive teaching and learning practices for larger classroom settings. Create a learning and training environment that promotes social responsibility, active engagement, and the well-being of its communities in the pursuit of health equity.
• Assessment: Evaluate and provide constructive feedback on student performance and assessments in a timely manner. Ensure fair, transparent and consistent assessment practices in the spirit of Brown’s Code of Conduct and Open Curriculum philosophy.

Qualifications:

• Education: Master’s degree or higher in Biostatistics, Statistics, Data Science, Biomedical Informatics, or a closely related field. A Ph.D. is preferred but not required.
• Experience: Course development and teaching experience at the graduate level is required, and experience teaching computational approaches to groups of students with varying prior training will be viewed as a major strength. Candidates with prior experience in the development and use of statistical methodologies to analyze biological or health data in real world settings is strongly preferred.
• Communication: Excellent verbal and written communication skills. Ability to engage and motivate students with diverse backgrounds and skill levels.
• Commitment: Strong commitment to inclusive teaching practices and high-quality teaching. Ability to work collaboratively with faculty and staff.

How to apply

Applications will be reviewed as they are received and continue until the position is filled or the search is closed. Please apply online through Interfolio. Please upload the following: (i) a letter of application stating specific qualifications for the position, (ii) a teaching philosophy statement, (iii) a summary of teaching evaluations where applicable, and (iv) an updated curriculum vitae. Applicants should arrange for at least two letters of recommendation to be uploaded to the online application; with at least one letter specifically addressing the applicant’s course development and teaching skills in biostatistics or a closely related field. Brown University is committed to increasing diversity and inclusion of underrepresented groups. If there is additional information not included in your application materials that might help to demonstrate your contribution and commitment to diversity, equity, and inclusion, we welcome you to share it in your cover letter. Full consideration will be given to all applications received by October 31, 2024. Applications received after the priority deadline may be reviewed until the position is filled or the search is closed.

As an EEO/AA employer, Brown University provides equal opportunity and prohibits discrimination, harassment and retaliation based upon a person’s race, color, religion, sex, age, national or ethnic origin, disability, veteran status, sexual orientation, gender identity, gender expression, or any other characteristic protected under applicable law, and caste, which is protected by our University policies.