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Field
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Design and execute controlled hydraulic stimulation and production experiments with different shut-in protocols Quantify how different shut-in strategies influence seismicity rates and magnitudes Derive
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learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch) using high-performance computing
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processing, computer vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. The University may permit
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wide differences within human gut bacteria species in early childhood. Our goal is to improve the understanding of adaptive mechanisms employed by different gut bacteria at a high resolution to guide
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programming experience is a plus. Programming Skills: Strong experience with MATLAB and/or Python; experience with C++ for high-performance signal processing is a plus. Experimentation: Proven experience
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databases and structured and unstructured data mining tools Knowledge of SAS, R, Python, and SQL, or data visualization software Knowledge of open/public/private databases HOW TO APPLY: Applicants should
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statistical and geospatial analyses (e.g., using ArcGIS, SPSS, Stata, Python, R). Conduct cross-city comparative research Map and analyse the geographical distribution of eviction patterns within and across
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AWS Management Console, AWS data storage (S3) and virtual machine (EC2) to enable research collaborators through Research Gateway. Enable AWS cloud infrastructure for different researchers within and
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technologies and photonics components: Speciality fibres & low-loss fibre connectors: Analyse different layouts to maximize light coupling into the fibre and to provide stable, high signal-to-noise output
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quantum fields from disordered and complex media. Student researchers will be involved in different aspects of the projects including numerical and computational level to hands-on experimental work