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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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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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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
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) or a similar degree with an academic level equivalent to a two-year master's degree.[BN3] [SK4] Strong programming skills in Python and MATLAB Background in biomedical signal processing, ML, and BCIs
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skills (e.g., Python, Java, or similar) Ability to work independently as well as in an international research team Excellent command of English, written and spoken. You must have a two-year master's degree
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/scripting (e.g., Python, R, or Bash) Familiarity with next-generation sequencing data and genome assembly tools Strong analytical and problem-solving skills Excellent written and spoken English communication
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SQUID magnetometry or similar techniques is a strong advantage. Experience in programming, ideally using Python to the extent that you can independently write programs to control and automate measurements
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, Management Science, Analytics and Data Science Knowledge of and practical experiences with programming (e.g., Python, Java, Julia, etc.) You must have a two-year master's degree (120 ECTS points) or a similar
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deep learning, preferably including some exposure to graph neural networks or geometric deep learning. Proven experience with implementing machine learning methods in Python and Pytorch. Familiarity with