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Field
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Familiarity with statistics and programming experience in Python are advantageous Strong intention to be a part of international team with interdisciplinary questions We offer: An interesting and vibrant field
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to biological data sets such as omics data, protein structure prediction, or biomedical imaging. Technical experience in programming (Python preferred), and/or machine learning is a plus—not a requirement. We
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Experience Experience developing research software using appropriate languages and environements (Python, Julia, Matlab) Knowledge of optimisation problem formulations and solution methods Experience of risk
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framework and execution of projects > Understanding of financial products, market dynamics, and microstructure > Low-level computer languages like C++ or Python, Java, etc.; awareness of strength in
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framework and execution of projects > Understanding of financial products, market dynamics, and microstructure > Low-level computer languages like C++ or Python, Java, etc.; awareness of strength in
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, AquaCrop) or experience with agricultural or environmental modelling. Some experience with programming in R and/or Python. Exposure to climate or weather data, forecasting systems, or geospatial tools
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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starting the PhD). The candidate must be qualified for admission to the ph.d. program Strong background in quantitative methods (reflected in courses and/or research experience) Proficiency in R, Python
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in modelling using Matlab or Python. You have solid experience using process simulator AspenPlus or Hysys, and you have used them to simulate absorption processes. You have experience with absorption
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proficiency in R or Python for data analysis and modeling. Familiarity with analyzing large-scale healthcare datasets and real-world data. Experience in developing and applying simulation models, including