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- Delft University of Technology (TU Delft)
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have: MSc in engineering or similar discipline by the start date of the position Experience with mechanical modeling and simulation Experience in computer programming/scripting (e.g., C++, Python, Matlab
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programming skills are a plus: MATLAB, Python, Git Fluency in English An open personality and good communication skills in written and spoken English. TU Delft (Delft University of Technology) Working at TU
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will do Interpretable machine learning is a growing research area, with important applications in the biological sciences, such as understanding how different genes regulate each other within biological
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential
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skills. experience in data analysis, quantitative modeling and programming (e.g., R, python); knowledge of nutrient and/or agrochemical cycles in agriculture; excellent scientific writing skills in English
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interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
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implications for both fundamental and medical sciences. Job requirements MSc degree (or nearing completion) in physics, biophysics, computational biology, or a related field. Strong programming skills (Python
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work; Experience with data analysis, such as statistics, data management, etc.; Experience in scripting/programming (e.g., R, Bash, Python); Strong interest in understanding human impacts on ecological
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: Completed, or soon-to-be completed MSc in the biological sciences or different fields in the natural sciences (e.g. computational, mathematical, earth or marine sciences) with a strong interest in ecology and
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explore, and how different ways of structuring learning environments influence curiosity and learning. Computational models will be used to characterise individual differences in information-seeking