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or python. An interest in evolutionary biology, comparative genomics or the biology of early-diverging animals would be beneficial, although applicants primarily trained in molecular pharmacology, cell
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Divergence (MIND) approach for estimating structural similarity networks, which enables robust structural brain networks to be derived from T1-weighted images alone and has already been shown to be sensitive
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Instituto de Geografia e Ordenamento do Território da Universidade de Lisboa | Portugal | 8 days ago
Neighbourhoods with Different Levels of Accessibility" (Project Reference: DUT/0007/2022), funded by the Foundation for Science and Technology (FCT, I.P.). DOI: https://doi.org/10.54499/DUT/0007/2022
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results across different data types and projects, and uncover biological patterns and insights. Strong collaboration and communication across the computational and experimental sides of the lab will be
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in statistics, biostatistics, data science, applied mathematics, quantitative social science, or a closely related field. Strong Proficiency in Python or R for statistical analysis, data management
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Office, NI LabVIEW, Python, MatLab Strong, interpersonal, written and oral communication skills. Required Licenses & Certifications: Driver's License Knowledge, Skills & Abilities: Experience supporting
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Huntington’s disease neuroimaging machine-learning models. The position will design, build and document a responsive, serverless platform enabling Python-based models, including scikit-learn, LightGBM and
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and work closely with colleagues in the Immersive Learning Centre. You will need real technical depth: comfort with Python environments, command-line tools and model repositories, and a working
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interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
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around the modelling protocol: diagnostic checks of scenario submissions against the protocol, and documentation of harmonized assumptions and remaining differences across models Contribute to / develop