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, that can be realised using cloud compute infrastructure and other novel deployment architectures. Given our team's existing research skillset in novel machine learning approaches, we are recruiting a
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, that can be realised using cloud compute infrastructure and other novel deployment architectures. Given our team's existing research skillset in novel machine learning approaches, we are recruiting a
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, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor evolution, identify clinically
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expressions for latency and offloading probabilities across edge-only, cloud-only, and edge-cloud computation offloading algorithms. Design and develop robust Python APIs for edge-only, cloud-only, and edge
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goals on our lab website (https://hscrb.harvard.edu/labs/arlotta-lab/). In addition, the Arlotta lab is actively working to foster an equitable and inclusive community. Basic Qualifications · Ph.D. degree
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FAIR cloud platforms for real-time genomic data exchange Create interactive dashboards showing genomic trends, variant emergence and geographical spread Translate genomic findings into public-health
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, clear documentation, and continuous improvement of development infrastructure (e.g., CI/CD, Docker, cloud-based services). Carry out Risk Assessment, and ensure compliance with Work, Safety and Health
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in international research collaborations, scientific campaigns, and research mobility. Experience with research infrastructures and experimental campaigns involving the monitoring of clouds, aerosols
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. Experience using high-performance computing and/or cloud computing. Ability to work independently as well as collaboratively in an interdisciplinary research environment. Strong written and oral
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these efforts, we aim to accelerate the translation of molecular and clinical insights into tools that improve patient outcomes. More information about the lab can be found here: https://www.nathlab.org As a