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use the data to train deep learning models of cancer. This allows us to identify systems-level mechanisms that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. We
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use the data to train deep learning models of cancer. This allows us to identify systems-level mechanisms that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. We
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psychiatry. The projects will involve advanced epidemiology, pharmacoepidemiology, and machine learning methods. You will be part of a well-funded and successful research group, collaborating with
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span over the physical, media access control, and network layers. Methods from networking, communication theory, machine learning, signal processing, and optimization will likely play an important role
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and/or application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Tasks The tasks include primarily leading and conducting research
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and biology of infection with a strong computational profile. This research subject area aims to lead to innovative development and/or application of novel data-driven methods relying on machine
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and biology of infection with a strong computational profile. This research subject area aims to lead to innovative development and/or application of novel data-driven methods relying on machine
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such as epidemiology, biostatistics, computer science, statistics, etc. We will also consider those with PhDs in other areas but who have advanced/relevant data science skills (e.g., machine learning
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criteria, and should be shown through scientific publications/doctoral dissertation. Knowledge and experience of development within model reduction or machine learning. Ability to work both independently
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have the following skills: Excellent computer/ programming skills. Proficiency in Python, including object-oriented programming concepts Experience with modern software development and engineering