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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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, optimization, deep learning, or information theory; Experience in programming, e.g., in C++, Python or Matlab. Qualification requirements PhD stipends are allocated to individuals who hold a Master's degree. PhD
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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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analytical skills, including signal processing, statistical learning, optimization, deep learning, or information theory; Experience in programming, e.g., in C++, Python or Matlab. Qualification requirements
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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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, 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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renewable energy, e-mobility, or engineering Good programming skills (e.g., Python, MATLAB or R) Familiarity with control concepts Ability to work effectively with data and applied statistics Ability
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sample preparation Desirable Qualifications Prior experience with negative ion mode MS is an advantage Interest in computational proteomics workflows Programming or scripting skills (e.g., Python, R
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background in computer vision, and machine learning. Prior experience in 3D vision or related fields such as 3D reconstruction Proven software and debugging skills in Python and/or C++ Experience with learning