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results Your profile Completed university studies (Master) in the field of Chemistry, Radiochemistry, Geology, Mineralogy, Physics or related field Experience in experimental methods for analyzing reactive
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, especially attachment/detachment and flotation kinetics Basic familiarity with multiphase measurement systems (e.g., optical probes, inline endoscopy, interferometric methods) is an advantage Interest in model
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) and/or by applying ultrafast X-ray tomography (e.g., ROFEX) Develop and further improve multi-sensor and imaging-based measurement methods to capture key process variables in different zones
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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, Process Engineering, Computational Science, or a related discipline Strong foundation in fluid mechanics, gas–liquid two-phase flows, numerical methods (FVM, FEM), and two-phase flow instrumentation
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computational engineering, mathematics, computer science, physics, engineering or a related field Strong background in numerical methods and machine learning Proficiency in at least one programming language
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mechanisms using computer simulations and the methods of non-equilibrium statistical physics. The Research The PhD student will work under the supervision of Dr. Philip Bittihn within the European Doctoral
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PhD project involves interdisciplinary research at the interface of computer science and mathematics and addresses a complex, coupled inverse problem with explicit uncertainty quantification. Research
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timetable for the four-year project to be submitted to DAAD. Development of relevant analytical methods and setting up of required laboratory equipment will be conducted in parallel. Execution of the research
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imaging over multiple generations. In parallel we will build upon our new experimental and computational framework that links phenotype to function at the single-cell level (named “Look-Seq2”). Strong