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                Field
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                to combine ribonucleic acid (RNA) research with infection biology. Based on novel findings from our strong basic research program, our long-term goal is to develop innovative therapeutic approaches to better 
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                Your Job: Your PhD project will be part of an interdisciplinary research project bridging theoretical physics and theoretical computer science. In the course of this, you will work on extending 
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                bridging theoretical physics and theoretical computer science. In the course of this, you will work on extending established techniques for quantum circuit representation and manipulation (e.g. diagrammatic 
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                instruction is English. All courses, lectures, and workshops are held in English. Participants are required to write and defend their doctoral thesis in English. Programme duration 8 semesters Beginning Winter 
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                grades in the field of mechanical engineering, material science, physics, computational science or similar, with experience in modeling and simulation Strong understanding of the (computational) mechanics 
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                can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried out within the DFG Priority Programme “DaMic - Data-driven Alloy and 
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                English Languages Courses are usually held in English. The dissertation is written in English. Mode of study Less than 50% online Programme duration 6 semesters Beginning Only for doctoral programmes: any 
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                funding phase at the Martin Luther University Halle-Wittenberg will start with a highly interdisciplinary and ambitious research program in November 2025. We invite applications for 8 PhD positions for 3 
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                (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research 
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                diverse working day is guaranteed! During the project, you will develop and implement self-learning control algorithms that balance computational demand and modeling precision. You will evaluate, interpret