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Field
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential
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discipline. Programming & Data Skills: Strong proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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understanding of how acoustic waves are generated and transmitted in wells. The LeDAS project aims to overcome these challenges by combining physical modelling, advanced signal processing, and machine learning in
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of immune cell function. These projects are focused on making safer and more effective cell therapies (e.g., CAR-T) and gene therapies for cancer and beyond. We are an interdisciplinary lab spanning
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. Machine learning algorithms and physics of failure modelling LanguagesENGLISHLevelExcellent LanguagesFRENCHLevelBasic Additional Information Benefits MSCA Postdoctoral Fellowships enhance the creative and
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/Research Fellow(SRF/RF) to carry out research in robotics and machine learning by exploring cutting-edge approaches such as learning-based robot perception, adaptive control with reinforcement learning
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modeling, machine learning methods, and applications involving text and other non-traditional data sources. The fellow will contribute to and extend research in these areas, engaging in projects that develop
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Website: https://engineering.uark.edu/ Summary of Job Duties: The Postdoctoral Fellow will continue investigation of cancer nerve crosstalk in both breast cancer and pancreatic cancer. In both projects