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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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-throughput data, particularly next-generation sequencing data. Experience in the analysis of Hi-C and HiChIP data. Very good knowledge of at least one programming language used in data analysis, such as Python
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opportunities. The chance to help shape a newly funded research line with real clinical impact, within an international network of academic and industry partners. Where to apply Website https
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systems. This includes programming (e.g., Python, R, MATLAB), quantitative data analysis, and development and application of mechanistic, deterministic models. Instructions To ensure full consideration
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analysis (FEA) and numerical modelling, preferably using Abaqus. Good programming skills, preferably in Python. Ability to work effectively both independently and as part of a multidisciplinary research team
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Plasmodium genomic data analysis; see https://mrc-ide.github.io/PGEforge/. Data partitioning: The 24,409 analysis-ready samples will be extracted and partitioned into subsets by country (since this is the most
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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to cloud infrastructure in Docker containers. Our technology stack is published in our Guidebook and comprises technologies like Google Cloud, Terraform, Docker, Python, Django, React, and TypeScript. We
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Telegraph (a hands-on lab module for telerobotics and haptics) • Working knowledge of robotics packages: Python libraries (e.g. Corke) and NVIDIA Isaac for Healthcare (e.g. Newton enabled medical physics
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, e.g. in Python, particularly for machine learning Experience with machine learning theory, evaluation metrics, algorithmic fairness, statistics, or optimization is an advantage Language requirement