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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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) 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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, 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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/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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and applying new skills. Experience with programming in Python and a working knowledge of statistics It would further be beneficial if you have some of the following skills: Hands-on experience with
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decision-support or safety-critical systems. Knowledge of Cyber-Physical Systems, Sensor Fusion, or Risk Management is an advantage. Excellent programming skills (Python, C++, or similar). Ability to work in
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Solid experience with statistical modeling, machine learning, or AI Practical skills in R and/or Python for data analysis and model development Familiarity with microbial ecology, genomics, or food safety