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(Python preferred) for bioinformatics or data science applications. Demonstrated track record with single-cell technologies (experimental and/or data analytics). Approval and Enrolment The scholarship
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• Knowledge of statistics and/or data science • Knowledge of Matlab, Python, or a similar programming language • Some research experience (e.g., in the form of a conference contribution, a publication, or a
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, mathematics, computer science or similar; Solid mathematical and analytical skills, including signal processing, optimization, or information theory; Experience in programming, e.g., in C++, Python or Matlab
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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Programming skills in Python, R, and/or GIS tools Highly valued: Background in LiDAR point-cloud analysis and vegetation structure analysis or habitat monitoring Experience applying AI or machine learning
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for Intelligence analysis. Implement Agent-based Modeling tools in Python. Develop and implement novel tools for Intelligence analysis. Carry out benchmark analysis, evaluating improvements over traditional
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programming skills (R, Python, or Matlab), particularly regarding bioinformatic tools. In addition, you are expected to have insight into plant biomass composition, chemistry and analytics, some practical
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and Computer Vision Excellent programmer in Java / C / Python or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical
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, mathematical and programming contexts Your research will include extending and contributing to models and codes, including both high- and low-level programming languages, e.g. Python/Matlab to the development
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for Science & Technology (KAIST), and an external stay at KAIST will be included as part of the PhD program. Qualifications Proficiency with Python Experience implementing various Machine Learning algorithms