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of climate model output by means of classical statistical and machine-learning methods #coordination of scientific workflows among project partners Your profile #Master's degree and PhD degree in meteorology
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Director of the GIBH Center for Cell Lineage Atlas,with research expertise in cell lineage dynamics. Deputy Director of the GIBH Center for Biomedical Digital Science, with research expertise in AI/machine
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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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and Fluidigm technologies at UTHSC. Qualifications PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, or a related field. Strong background in machine learning, data
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bioinformatics, with a particular emphasis on performing analysis of high-dimensional data, which can be sequencing and/or imaging-based. Experience working with AI and machine learning approaches are considered a
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protein design and evolution, using molecular biology and biophysics, along with the latest AI or machine learning tools. According to the development of the project, there may be the chance to learn other
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theory, and machine learning to quantify and understand cancer biology. We are seeking a highly motivated Postdoctoral Researcher to develop new computational methods for the analysis and interpretation
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of extension for three years. Tiwari lab employs cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation. In this project
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found on hpc.uni.lu . The activities include classical HPC applications such as simulation and modeling, but also artificial intelligence and machine learning, bridging computational science, with data
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Postdoc position (f_m_x) ,,Combining Physics-Based Machine Learning and Global Sensitivity Analys...
“Geosystems”), we are looking for a: Reference Number 10337 Are you seeking a PostDoc project at the interface between geoscience, machine learning and mathematics – with an application to the highly relevant