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molecular properties for comparison with experimental results. Requirements: Completed university degree (M.Sc or Diploma degree) in Chemistry or a closely relatet field and PhD / Doctoral degree in Chemistry
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: According to §110 (4), sentence 3, the BerlHG provides academic staff with an appropriate amount of time within their working hours for their own further academic qualification. What we are looking for PhD
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collaboration with our clinical colleagues for investigating visual motion in a group of healthy subjects. The successful applicant holds a PhD degree (or equivalent) in a relevant academic area such as applied
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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projects of the research group Qualification Required: A diploma and a PhD in microbial natural product chemistry Hands-on experience and a strong understanding of metabolomics/dereplication of natural
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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algorithms, mechatronics, intelligent robotics and prosthetics, robot learning algorithms, foundations of machine intelligence, as well as nonlinear control and systems theory. Furthermore, we offer unified
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areas is expected: numerical analysis, scientific computing, model reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming
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change #utilize a participatory system dynamics modeling to match resilience patterns with best-fit learning cases from various regional contexts in Europe #develop a resilience performance framework for
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research collaborations with colleagues at the institute. Additionally, we expect postdocs to teach one course per semester. What we expect from you: An excellent PhD in Psychology, Education Sciences