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pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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of tools such as R, Python, GIS, Git or similar data-science software. Solid experience with community data and biodiversity monitoring. A broad ecological background, ideally including plants and
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quantitative genetics or animal breeding Has published high-quality research in peer-reviewed journals Experience with scripting languages (e.g., R, Python, SAS) and/or genetic software (e.g., DMU, ASReml) Can
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must be proficient in programming in Python and Java script. Have strong cooperation and communication skills. Have good command of the English language, both spoken and written. Be independent and
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qualifications: Applicants must hold a PhD degree in computer science, bioinformatics or similar. The applicant must be proficient in programming in Python and Java script. Have strong cooperation and
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the following areas. You have a background in tissue-based molecular research and experience with tissue sectioning and the generation and analysis of spatial molecular data. Programming expertise in Python and R
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skills in Python and experience with deep learning frameworks (e.g., PyTorch); Experience with distributed systems and edge AI; Strong publication record in reputable conferences or journals relative
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, or a related field Strong experience in spatial and/or landscape modelling Proficiency in R and/or Python Experience with GIS and remote sensing Ability to work with large and heterogeneous datasets
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analysing large health datasets, electronic health records, UK Biobank, All-of-Us, or similar sources. Experience with programming in R, Python, C++, Stata, SAS, or other programming languages. Excellent
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field Knowledge of freshwater ecology and/or physical limnology Experience with numerical modelling Experience with programming languages, esp. Python, and familiarity in NumPy, SciPy and Pandas