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Field
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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Deutsche Forschungsgemeinschaft within the Reinhart-Koselleck programme for innovative high risk-high gain research. Requirements: university degree in chemistry or physics and profound knowledge in
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from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference
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At the Fraunhofer Institute for Applied Solid State Physics IAF, we know our technologies like the back of our hand. Because we are one of the few scientific institutions worldwide to conduct
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 24 days ago
breeding populations to investigate the genetic basis of traits such as visual anther extrusion, heading date, plant height, and grain yield. You process genotypic data, ensure its alignment with phenotypic
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if an adequately stamped envelope is attached. If you apply, we collect and process your personal data in accordance with Articles 5 and 6 of the EU GDPR only for the processing of your application and for purposes
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programme No Description/content The IMPRS for "Quantum Dynamics and Control" is a graduate school with its headquarters at the Max Planck Institute for the Physics of Complex Systems in Dresden/Germany and
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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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Your Profile: An excellent master’s degree with strong background in nano science, materials science, chemistry, chemical engineering, physics or related Experience with modeling of reaction kinetics
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associated PhD topics is highly encouraged Your profile Completed university studies (Master/Diploma) in STEM fields with strong background on process modelling, applied mathematics, process engineering