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are looking for candidates with strong foundations in modern machine learning and the ambition to build brain foundation models and other AI systems that advance our understanding of neural activity, brain
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models, report formats and other analysis considerations, determine and write statistical considerations and algorithms for protocol documents according to study design and appropriate statistical methods
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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animals, daily rounds, and observations to check animal health status; adherence to protocols and techniques at the animal facility; performing and documenting experimental observations in rodent species
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rodent (rat/mouse) models. The Research Fellow will work closely with a multidisciplinary team to design, build, and test sensor systems for recording neural and physiological signals. Responsibilities
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and machine learning based analyses including predictive modeling and real world evidence generation. Basic Qualifications: MS in computer science, biostatistics, biomedical informatics or related field
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sequence-function landscapes, using a combination of empirical data and ML methods (e.g. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern
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interdisciplinary approaches that combine advanced microscopy (confocal, electron, in vivo multi-photon), viral vectors, protein engineering, mouse models, and multi-omics analyses. For further information on the lab
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), library research (Pubmed searches, systemic review methods), and statistical analysis (data visualization, descriptive analyses, time series analyses, latent modelling, multilevel modelling); Project
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), library research (Pubmed searches, systemic review methods), and statistical analysis (data visualization, descriptive analyses, time series analyses, latent modelling, multilevel modelling); Project