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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months ago. Learn more: https://www.cagenome.org/lab How to Apply Please submit a single PDF containing your CV, a cover letter describing your research interests and career goals, and the contact information
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infrastructures. You will contribute to research projects enabling secure, interoperable, and scalable use of clinical data for AI and machine learning applications in complex diseases such as Cancer, Alzheimer's
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Group invites applications for a Postdoctoral Fellow position. The research will develop and utilize emerging machine learning tools to remove bottlenecks in many-body simulations. Responsibilities: 1
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only - to learn more, visithttps://www.abbvie.com/join-us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: https
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, machine learning, and data science. The successful candidate will work with Dr. Yohanna Mejia Cruz on the development of sensing and computational frameworks to characterize human mobility and behavior
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assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu Employment Requirements Any offer of employment
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) Life and Disability Insurance Paid Time Off: 20 days of vacation per calendar year, 12 days of personal/sick time Paid Holidays and Special Days Off: https://www.bowdoin.edu/hr/our-workplace/college
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Enthusiasm and commitment to pursue novel research areas and develop new techniques and approaches. Team player with an interest in learning to mentor and train other Lab members. Ability to think
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Associate to lead the research operations of The Future of Higher Education and Work at Northeastern University: Personalized Learning for Students and Customized Career Preparation , a study funded by the
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and machine-learning potentials for planetary materials. Curating and generating large-scale ab initio datasets across wide pressureâ“temperature regimes. Designing and training advanced machine