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.
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.
Qualified candidates will have Subject Matter Qualifications in the following areas.
Required:
Preferred:
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.
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.
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")
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