Brandeis University’s Graduate Professional Studies (GPS) is looking for an industry leader to teach in our Master of Science in Applied Biotechnology & Enterprise program. Brandeis University is consistently ranked among the nation’s top universities, and our online courses are developed using best practices in online learning. Information about Brandeis University and Graduate Professional Studies can be found online.
Course Title: Artificial Intelligence & Machine Learning in Drug Development
This course explores the use of AI and ML in pharmaceutical R&D. Topics include data preprocessing, predictive modeling, and applications in target identification, drug screening, and clinical trial optimization. Students gain hands-on experience using tools and algorithms to analyze real-world biotech data sets.
Qualifications
Qualified candidates will have Subject Matter Qualifications in the following areas.

Required:

• Current active employment in the relevant biotech or pharmaceutical industry
• Deep knowledge of course-specific applications and industry practices
• Ph.D. or M.S.
• Strong communication and analytical skills

Preferred:

• Previous teaching experience or experience delivering professional training
• Familiarity with online instructional tools and platforms

General Responsibilities

• Design a syllabus following program chair guidance and the syllabus template
• Create content aligned with course outcomes, integrating personal expertise
• Facilitate online discussions and provide timely, relevant feedback
• Maintain the course site and update content in the Moodle LMS
• Lead students in AI model development and interpretation using biotech case studies.
• Help students critically assess the limitations and ethics of AI in pharmaceutical innovation.

General Skill Requirements

• Strong interpersonal skills and the ability to foster student engagement
• Written communication skills, especially for online learning
• Ability to manage time effectively and respond promptly to students
• Proficiency in applying machine learning tools (e.g., Python, scikit-learn, TensorFlow) to drug discovery datasets.

About the Master of Science in Applied Biotechnology & Enterprise
This program prepares students to innovate and lead in the fast-paced biotech industry, integrating scientific knowledge with business strategy. Students learn through project-based, real-world applications.
All GPS courses are 8-weeks long and taught asynchronously online.
About GPS Faculty
GPS Faculty are active industry professionals who teach part-time, online. Our instructors hold at least a master’s degree, and many have terminal degrees and professional certifications. Previous teaching experience is not required; GPS provides full training.
Application Process: Interested candidates should submit:
A cover letter highlighting relevant qualifications and teaching experience.
A current CV or resume.
Contact information for three professional references.
(Optional) Examples of teaching materials or professional publications related to leadership and industry innovation.
This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509.
Equal Opportunity Statement
Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a “protected class")

• Design a syllabus following program chair guidance and the syllabus template
• Create content aligned with course outcomes, integrating personal expertise
• Facilitate online discussions and provide timely, relevant feedback
• Maintain the course site and update content in the Moodle LMS
• Lead students in AI model development and interpretation using biotech case studies.
• Help students critically assess the limitations and ethics of AI in pharmaceutical innovation