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of ecological, coastal, and geological research as well as perform analyses with Remote Sensing (optical and lidar), Geographic Information Systems (GIS), Python, R, and/or other programming languages or image
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(e.g., Matlab, Python), neuroinformatics, and/or advanced microscopy tools is plus, but not required. More details of the lab including publications can be found in the lab web site (https://kimlab.io
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theoretical understanding of statistical machine learning methods relevant to the project: Bayesian learning, machine learning, spiking neural networks. Experience of programming (e.g. with Python) and data
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Boltzmann method (LBM), discrete Boltzmann method (DBM), and other kinetic models. Solid knowledge of fluid dynamics and heat transfer. Proficiency in programming languages such as C/C++, Fortran, Python
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/co-first-author original research papers. - Strong programming skills in Python or R, and Linux Shell. - Creative, independent, and highly motivated, with good communication skills Preferred
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, bioinformatics, molecular biology, toxicology, pharmacology). 2. Proficiency in bioinformatics, programming, and tools for the analysis of large genomic datasets and single-cell dataset, including R, linux, python
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred
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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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software skills in C++, python. Understanding of computing software development in the HEP environment, familiarity with machine learning (ML) techniques and experience with using ML software packages (e.g
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programming languages (e.g., Python, MATLAB, R) for complex data analysis Excellent written and verbal communication skills Ability to work both independently and collaboratively in a research setting Preferred