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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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in this position will conduct/lead applied as well as fundamental research in physics-informed Artificial Intelligence (AI) and Machine Learning (ML) methodologies enabling digital twin functionalities
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Transduction Job Description Saint Louis University is a Catholic Jesuit Institution dedicated to student learning, research, health care, and service. This Postdoctoral Fellow position is in the chemistry
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candidate, you will: Develop and apply Bayesian Network machine learning methods to analyze the dynamics of G-protein coupled receptors to uncover allosteric regulation that enables design of allosteric
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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focus of the position is on application and advancement of modern artificial intelligence (AI) methods in drug discovery and development. An ideal candidate will have strong background in machine learning
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Human-Computer Interaction, Information Science, Computer Science, Design, or related fields - Strong record of published research in HCI, CSCW, DIS, or related venues - Demonstrated ability in
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Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English
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ATAC sequencing, spatial transcriptomics, proteomics, whole-genome sequencing, functional screens, bioinformatics, and/or data algorithms including machine learning will be given preference. A successful