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programming languages (e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability
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Post Doctoral Researcher in Human-centred Large Language Models for Software Engineering, Departm...
. Required qualifications: A Ph.D. degree in Computer Science, Data Science, Software Engineering or related field. Solid research experience with using Large Language Models. Solid programming expertise in
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of cellular aging, resilience, and fibrosis. Responsibilities Develop and implement analytical pipelines for large-scale single-cell, spatial, and multi-omics data integration Build and apply machine learning
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experience is beneficial Experience with the analysis and interpretation of large data sets, in particular high-throughput sequencing data from eukaryotic organisms Solid skills in programming and scripting
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research funding than all other New Jersey universities and colleges combined. Rutgers manages Protein Data Bank (PDB), the global archive of 3D structure data for large biological molecules (proteins, DNA
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Centre (HDSC) focuses on systematically generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information enabling a better understanding of the clinical
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multi-modal models, contributing to bridge transport economics, network modelling and activity-based modelling, and leverage different types of (big) data. The applicant should be a creative and motivated
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environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental sustainability. You will focus on processing
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Experience with writing scientific publications Experience in working with contaminants like PFAS, PCBs and mercury, fatty acids, stable isotopes and modelling in R A talent for working with large data sets
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and applying genetic and genomic approaches to biodiversity research. This includes integrating environmental DNA (eDNA) and molecular tools with ecological data to enhance our ability to assess