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this project, you will develop innovative quantum algorithms for the accurate calculation of materials properties. You will combine methods from quantum informatics and solid-state physics to describe
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include: Applying controlled stress treatments Phenotyping stress responses with various sensors and gene expression studies in real-time PCR RNA sequencing and transcriptome analysis with corresponding
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data analysis (e.g., Python, R, C++, MATLAB), computational modeling, imaging and sensor data processing, bioinformatics, systems biology, or biophysics. Familiarity with simulation environments
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one of the following areas: scientific programming and data analysis (e.g., Python, R, C++, MATLAB), computational modeling, imaging and sensor data processing, bioinformatics, systems biology, or
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particular focus on the biosynthesis pathways of secondary plant metabolites (flavonoids). Your tasks will include: Applying controlled stress treatments Phenotyping stress responses with various sensors and
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research on an international stage To apply, please submit a complete CV, letter of motivation, university
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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study qubit systems. A particular emphasis is on exploiting the manipulation capabilities of scanning probe microscopes to fabricate molecular quantum sensors on probe tips to detect the tiny electric and