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interdisciplinary desire to learn and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills as well as the absolute determination
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the field of biotechnology, bio-/chemical engineering, (bio) process engineering, bioinformatics, biophysics or biomathematics. Ideally you have Programming skills and knowledge on machine learning and
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are a small international team based at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. We aim to understand how molecular machines select transmembrane cargo proteins and
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quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent spoken and written English and the
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science. A wide range of quantum theoretical methods shall be employed. A solid background in quantum mechanics and programming skills are prerequisite for this position, as is the readiness to learn and to
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. - Neural networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In
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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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qualifications. You will teach students in accordance with the teaching regulations of the state of Hesse in the subjects “Animal Physiology” and “Neurobiology”. You will carry out research projects with a focus
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to doctoral researchers aiming for successful careers in science. Our comprehensive curriculum allows our students to tailor their learning to their interests, requiring them to earn 25 ECTS through various
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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI