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years. Preferred skills: Demonstrated ability to publish research findings in peer-reviewed journals Experience in bioprocess optimization and scale-up Ability to research independently, troubleshoot
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addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our
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that the spending is optimized and on track concerning goals set for the grant Develop ideas for application of research outcomes. Contribute to knowledge exchange activities with external partners and collaborators
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models, and foundation models Strong programming skills in Python and deep learning frameworks such as PyTorch Experience with large-scale experiments, model optimization, and performance analysis
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Demonstrated expertise in small-molecule synthesis, optimization, and structure–activity relationship (SAR) studies Strong problem-solving skills and the ability to work independently within a collaborative
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applicable to the position: Developing, testing, and analyzing research related to chemical sensing technologies in water; designing, fabricating, and optimizing sensing platforms and functional materials
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optimal control approaches and conduct test-bedding • Collaborate with project partners and mentor junior researchers • Prepare project reports and publish research findings with industry engagement Job
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department is seeking a highly motivated and skilled Postdoctoral Fellow to join a dynamic, interdisciplinary research team. The position focuses on the development, optimization, and integration of advanced
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to algorithms with actionable performance guarantees. More specifically, the research will revolve around the following theme: High probability convergence in stochastic optimization under heavy-tailed noise
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their optical properties. - Analyze species sampled in flames using laser-assisted mass spectrometry and optical spectroscopy; - Deduce the optimal operating parameters of the reactor to form species relevant