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, and present experimental data in a rigorous and reproducible manner. · Proficiency with standard computer applications and scientific data-analysis software, such as GraphPad Prism, R
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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/articles/s41467-020-18059-7 ), that enables localized, hyper-efficient delivery of therapeutic compounds to specific brain regions without invasive surgery. Project background We develop advanced focused
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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is to be the “EO innovation hub” connecting EO with a growing ecosystem of disruptive and transformative innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and
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complex or high-dimensional systems. Experience with physics-informed or constraint-based machine learning (e.g. neural ODEs, energy-based models) Experience with dynamical systems, stochastic processes
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, ventriculomegaly). Experience in machine learning statistical methods. Experience in the acquisition of infant neuroimaging data. Prior experience working with infants and children in a research setting. Enthusiasm
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and present experimental data. This includes applying computer analysis and simulation programs to interpret experimental data and characterize battery electrode materials and interfaces. Adapt and