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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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highly interdisciplinary, integrating big data analysis, state-of-the-art machine learning models, mathematical modeling, and systems biology to elucidate the mechanisms of drug interactions in complex
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modeling are applied. To learn more about the lab: https://www.mdanderson.org/research/departments-labs-institutes/labs/xufeng-chen-laboratory.html The incoming fellow will receive training and conduct
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/10.1126/science.adm8203. 2. Keleş, M.F., Sapci, A.O.B., Brody, C., Palmer, I., Mehta, A., Ahmadi, S., Le, C., Tastan, Ö., Keleş, S., and Wu, M.N. (2025). FlyVISTA, an Integrated Machine Learning Platform
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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Researcher to join our team in reimagining how we discover and deploy drug combinations in the clinic. Our work is highly interdisciplinary, integrating high-throughput screening, state-of-the-art machine
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have experience in computational neuroscience and data mining using machine learning methods. The successful candidate will lead an independent research project dedicated to identifying abnormal neuronal
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged