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Field
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knowledge of basic bioinformatics analysis (e.g., RNA-seq analysis, public dataset mining, pathway analysis) is preferred. A strong collaborative mindset. This is a one-year postdoctoral appointment, with
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School of Mechanical and Mining Engineering, Faculty of EAIT Full-time (100%), fixed-term position for up to 12 months Academic Level A or B (Postdoctoral Research Fellow / Research Fellow) Level A
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computational methods expertise, including Python, R or similar languages, machine learning, NLP, text mining and reproducible coding practices. Experience analysing social media data using qualitative
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and adoption of interventions that respond to changing needs of the region and its people around climate change, energy, mining, soil and water contamination, food production and livelihoods
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computational methods expertise, including Python, R or similar languages, machine learning, NLP, text mining and reproducible coding practices. Experience analysing social media data using qualitative
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. Demonstrated expertise in sentiment analysis, opinion mining, or computational analysis of media and public discourse. Candidates should be able to connect computational analysis with communication theories and
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environment where your work contributes to shaping sustainable mining practices globally. As part of the Centre for Environmental Responsibility in Mining (CERM), you will work alongside an interdisciplinary
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
interactions. These directives require a multifaceted approach involving physiological, biochemical, and molecular experiments and the use of numerous techniques such as RNA-seq, data mining of DNA and protein
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. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic modeling
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intricate. Specializing in program analysis and software analytics, the lab works on enhancing the precision and scalability of static analyses and mining insights from software repositories. Ongoing research