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problem-solving mindset. The ambition to conduct research on the interface generative AI and chemictry is essential. Candidates with experience with machine learning are encouraged to apply, while
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AI and mass spectrometry is essential. Candidates with experience with machine learning are encouraged to apply, while experiences in generative models and high resolution mass spectrometry
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culture, fluorescence microscopy, image analysis, mathematical modeling of dynamical systems, and machine learning is advantageous. Priority will be given to candidates with the overall highest experience
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approaches (QM/MM, molecular dynamics, free energy methods, machine learning). The PhD candidate will work closely with other PhD students, postdocs and senior scientists of the lab in an interdisciplinary
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approaches (QM/MM, molecular dynamics, free energy methods, machine learning). The PhD candidate will work closely with other PhD students, postdocs and senior scientists of the lab in an interdisciplinary
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seek a candidate with a keen interest in fundamental physics with technical aptitude in mathematical methods of theoretical physics, who is motivated to learn new skills and take part in developing new
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background in computational hydrological and/or hydro-climate research with a focus on extreme events Programming, hydrological modeling, remote sensing, machine learning and spatial data analysis skills A
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machine learning within fundamental physics, astrophysics, particle physics or cosmology. Main responsibilities Research and in addition some teaching and supervision. Qualification requirements In order to
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and teaching. Teaching is conducted both on campus and through distance learning, with a total of approximately 1000 students studying at IGV per year. The research activities primarily involve chemical