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-of-the-art IT infrastructure. Ideal candidates should hold a PhD in the area of Computer Science or Electrical and Computer Engineering and have strong programming skills including Python. Past work in
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programming, preferably python, and will ideally have experience in working with output from global models and/or IAMs. The candidate should be capable of working both independently and collaboratively and have
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well as to other tasks related to this research program in order to solve important problems. He/she will likely implement different algorithms in Python and potentially other programming languages. The postdoctoral
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about new methods, is a plus. Strong programming skills in Python or R, and Linux Shell; experience in genomics data processing and open source repositories e.g. GitHub is a plus. Strong motivation and
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the umbrella of the public sector innovation initiative at Duke University. Solid background in programming (e.g. Python) is required. The goal is to find a solutions to consider materials infrastructures in
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Science, Geography, or a related field. We are looking for candidates with a strong interest in ecology and quantitative modeling, along with training in programming languages such as R, Python, and/or MATLAB
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, preferably python, and will ideally have experience in working with a relevant land-system model such as the Community Land Model, an atmospheric composition model such as GEOS-Chem, and/or satellite
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should include MATLAB, Labview, Python and/or C++. For consideration, please submit your CV, cover letter, and names and contact information of three references. Duke University is an Affirmative Action
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of epidemiological methods and study design Strong experience in statistical genetics, omics technologies and their data analysis Proficiency in programming language (e.g., R, Python), statistical software (e.g. SAS
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, neuroscience, physiology, physics, or computer science · Be a proficient programmer and experience in Python, MATLAB, NEURON, COMSOL, and / or git are assets · Be familiar with neural biophysics and