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pipelines in R, Python and HPC or cloud environments. Collaborate with developmental biologists to interpret genomic findings, validate results and address key evolutionary questions. Support and mentor PhD
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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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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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such as SQL and PostgreSQL, and may leverage cloud-based data platforms such as Databricks. Project 2: AI Adoption and Integration You will collaborate with PHI mentors to research and investigate
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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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learning, generative AI or LLM-based systems, cloud computing, and the design and validation of algorithms for high-volume quantitative or sensor-derived data. Experience with wearable data, 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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, data pipelines, database management, SQL, REDCap, cloud or high-performance computing environments, and reproducible research practices. Excellent communication, stakeholder engagement and collaboration
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skills in scientific computing with Python and R; (d) experience in high-performance and cloud computing for data processing; (e) the ability to work independently and collaboratively with
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evidenced by publications or submitted manuscripts in refereed journals Experience with computational protein design (desirable) Experience working with cloud/HPC environments, workflow orchestration