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Job Description Project Description: Drug toxicity and resistance are the leading causes of therapeutic failures. The Chen Lab (https://www.stjude.org/research/labs/chen-lab-taosheng.html) studies
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational
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within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
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at the top venues of machine learning research. Responsibilities and qualifications You should have prior experience with machine learning from both a theoretical and practical perspective. Experience in one
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that after obtaining a PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding
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-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning frameworks that integrate
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communication skills. • Track record of conducting original research and publishing in peer-reviewed scientific journals. Preferred Qualifications • Experience in remote sensing or machine learning applications
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science methodologies (e.g., machine learning). Experience working with large-scale environmental and remotely-sensed datasets, strong proficiency in R and version control tools (such as GitHub), and
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representation learning. You have strong programming skills, especially in Python, and preferably experience with PyTorch. You have a track record of publishing in top image processing / computer vision journals