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Adjunct Lecturer for Marketing (MKTG217 Strategic Brand Management) - (26000094) Description Lee Kong Chian School of Business invites applications for adjunct lecturers to teach Marketing course
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Posting Details Student Title Classification Information Quick Link https://chapman.peopleadmin.com/postings/40719 Job Number SE236924 Position Information Department or Unit Name Financial Services
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back
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George Washington University | Washington, District of Columbia | United States | about 17 hours ago
programs. We particularly value candidates whose research incorporates emerging technologies, including AI, machine learning, or FinTech, or who can contribute to teaching in these areas. Specific Duties and
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Adjunct Lecturer, Finance (FNCE305 - Analysis of Derivative Securities) - (26000093) Description Lee Kong Chian School of Business invites applications for adjunct instructors to teach finance
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improvement initiatives and stroke program development Academic Responsibilities Teach and mentor medical students, residents, fellows, and other healthcare trainees Develop and participate in educational
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responsibilities Contribute to quality improvement initiatives and the growth and development of the Neuroimmunology and Multiple Sclerosis Program Academic Responsibilities Teach and mentor medical students
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variety of academic backgrounds and experiences. Teaching may include a variety of formats: face to face, remote, and online. Adjunct faculty are employed to teach up to 12 credit hours in the fall and
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project TARGETWISE. The candidate will be responsible for conducting machine learning omics data analysis within the computational team. The details on responsibilities, obligations and rights
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. Currently, DIMAG consists of researchers from various countries and the work is done in English. DIMAG is co-located with the IBS Extremal Combinatorics and Probability Group (ECOPRO). Website: https