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context. • Conduct statistical analyses, longitudinal modelling, or machine learning approaches as appropriate. • Develop documentation, codebooks, or tools to support reproducible research. • Lead
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been established. This position will focus on the further development of various, machine learning and deep learning models to study molecular mechanisms and cellular phenotypes caused by the etiology
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of an artificial intelligence (AI) solution for the diagnosis of invasive fungal infections, using microscopy images obtained in laboratory settings with limited resources. Leveraging deep learning models such as
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models with drone imagery using machine learning techniques and data assimilation. The work will involve collaboration with an interdisciplinary team of researchers, engineers, and local stakeholders in a
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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to analyze data and experience with statistical, machine learning, and data science approaches. Prior experience working in teams on collaborative projects. Knowledge, Skills and Abilities: Expertise in one
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for vehicle applications; Abilities in using Finite Element modeling and analysis; Knowledge of injury biomechanics; Knowledge of Artificial Intelligent (AL) and Machine Learning (ML) techniques; and Abilities
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with
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datasets Implement LLM and Machine Learning algorithm Conduct statistical analysis in SAS or Stata Assistant with other ad hoc tasks Required Education Bachelor’s or Master’s degree in computer science
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will