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, developing and applying AI technology to large medical images and genetic data for understanding brain development and aging, and precision psychiatry. In the past years, the laboratory has conducted various
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salaried and benefits eligible. Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines
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2021. The postholder will join Mario Giulianelli’s research group, which studies information processing in human and artificial systems. A core part of the group’s work also concerns the evaluation
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-performance computing and big data analytics PhD must have been received within the last three years. Preferred Qualifications: Demonstrated understanding of model validation, clinical trial design, and causal
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with BIG DATA (eg. NGS data in multi-TB scale). Experience using genome alignment software (bowtie2, bwa, tophat, etc.) is desired. Fluent in one programming language (Python, C, C++ or Java) and
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research data with variability at different levels or clustering Knowledge of missing data analysis using multiple imputation Experience and/or knowledge of health economics analyses within clinical trials
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
period of up to 30 months to contribute to the ARC Discovery Project, focusing on goal-oriented semantic wireless communications within the Information and Signal Processing Cluster at the School
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experimental X-ray scattering data. System Stability: Ensuring numerical reproducibility and stability in large-scale distributed training workloads. Learn more: Our benefits , where we prioritize your well
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technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word
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QTLomics as part of the project. Main responsibilities Collect and standardise functional information, including QTL data, RNA-seq, and Gene Ontology (GO) annotations Develop computational pipelines for QTL