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. You should have a strong academic background in engineering, applied mathematics, or computer science, combined with a clear interest in scientific programming, machine learning, and data analytics
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scientific backgrounds, including electrical engineering, industrial engineering, operations research, data science, and applied mathematics. Many of our former students are now successful scientists in both
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Laboratory techniques for sample preparation Analysis of mass spectrometric datasets and statistical calculations Contribute to in vitro analyses Contribute to carrying out animal experiments Contribute
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for empirical statistical analysis, as well as possible experimental designs to validate findings. Empirical setting The research will utilize data from the Danish Innovation Fund, integrated with databases