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intelligence (AI) residency) to advance scientific machine learning for clinical oncology. The project builds a hybrid framework that couples an existing quantitative systems pharmacology (QSP) model of cancer
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scientific conferences and publish models and scientific insights in high-impact journals Who You Are: Ph.D. in Computational Biology, Bioinformatics, Computer Science or Machine Learning related field
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discoveries. Who You Are: Ph.D. with a proven track record of excellence in Computer Science and Machine Learning, with substantial domain experience in biology and genomics. Must have advanced at least one key
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the Sterne-Weiler Lab i n Computational Biology / Discovery Oncology and co-mentored by the Frey Lab in Prescient Design (Machine Learning for Drug Discovery). The postdoctoral position is focused
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to lucidly present complex results, both verbally and in writing, to experts from different fields: computer scientists, computational biologists, biologists, and clinical audiences For more information about
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data to join the Departments of Proteomics and Genomics Technologies and Neuroscience at Genentech, Inc. We have in-house cutting-edge technologies including proteomics, lipidomics, NGS, CRISPR screens
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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challenges, including data analysis, hypothesis generation, and experimental design optimization. We seek a highly motivated postdoctoral candidate with expertise in AI, machine learning, and computational
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ability to thrive in a highly collaborative, interdisciplinary environment. Enthusiastic about leveraging innovative approaches to address big questions in tumor biology/ cancer signaling. Preferred
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communication skills, with the ability to present complex data to varied audiences. ● A collaborative mindset and the ability to work effectively in an interdisciplinary team environment. To learn more about the