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analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics. Project description Searches for dark
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and pair distribution function (PDF) analysis on carbon materials electron microscopy (SEM and/or TEM) data analysis, machine learning and molecular dynamics simulations of the structure and
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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Qualifications The following qualifications and experience will be considered an advantage: Experience with crop modeling. Experience with plant breeding. Background in data science, machine learning, and
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics