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students’ academic and mental health outcomes. Job responsibilities include: Conducting descriptive and advanced statistical analyses (including multilevel modeling) on extant and merged datasets using SAS
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Engineering). - Theoretical foundations of 6G RAN and autonomous systems o Proven knowledge of AI-native RAN systems. Indicative skills/experience: - Deep understanding of 5G/6G RAN architecture (O-RAN, NG-RAN
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AI and machine learning applications in health research. Demonstrated ability to manage large datasets and develop predictive models. Excellent written and verbal communication skills. Strong
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experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Minimum Requirements: Ph.D. in applied
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patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
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addition to this basic research, FTRG is involved in large community-based prevention efforts in both military and civilian populations. These studies use the full spectrum of research methods, from intensive
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professional development opportunities and strive to meet each individual’s development and well-being goals as much as possible. As an associate researcher with expertise in the field of machine learning within
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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for data augmentation, small language models, anomaly detection using learning methods, and explainability techniques for decision-making. The research will involve designing and developing prototypes
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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured