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Field
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with experience in One Health or climate-health research. Proficiency in systematic reviews, survey methods, and/or causal inference is essential, along with programming skills (e.g., in R or Python
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appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other
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in Environmental Modelling, Land Use modelling or another relevant field, with clear skills highly complementary to those of the JPP4JL research team Proven ability to write code in R or python
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. Preferred: • Strong programming skills in languages such as R/Python • Research background in biostatistics/statistical genetics/population genetics/deep learning and LLM • Experience in any of the following
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Chinese (Cantonese) and English, advanced knowledge and experience with statistical software (e.g., SPSS, R, Stata) and MS Office applications (including Word, Excel, PowerPoint etc.), and strong team work
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, or equivalent) Demonstrated experience with data analyses using Stata, SPSS, or R Effective oral and written communication skills Prior relevant publication in pediatrics research (minimum in press or under
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. Lozada-Hildalgo, & P. Unwin. Nature. 2023, 620, 782–786. 2. Thousand-fold increase in O2 electroreduction rates with conductive MOFs. R. Mariano, O. Wahab, J. Rabinowitz, J. Oppenheim, T. Chen, P. R. Unwin
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innovative research, UTA is designated as a Carnegie R-1 “Very High Research Activity” institution. UTA ranks No. 4 nationally in Military Times’ annual “Best for Vets: Colleges” list and is among the top 30
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procedures used in spatial data analysis and vegetation dynamics modeling is required. The candidate should have extensive practical experience in the use of R and GIS. Demonstrated skills of Bayesian modeling
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(Programming Language), R Programming Driver's License: A driver's license is not required for this position. More About This Job Required Qualifications: PhD in Genomics or a related discipline. Familiarity