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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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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 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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pump-probe technique, photophysics, or optical characterization of nanomaterials is highly desirable. Familiarity with programming for instrument control and data analysis—using platforms such as Python
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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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• 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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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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start) Due to engagement with participants and stakeholders, the PhD student must be fluent in Danish. Programming skills (e.g., Python) will be considered a strong advantage, particularly for handling
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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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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