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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: Join an interdisciplinary team that brings state-of-the-art AI
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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: A completed university degree (Master’s or equivalent) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including
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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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Your Profile: University degree (Master) in Physics, Chemistry, Computer Science, or related fields, with an overall grade of at least “gut” (or equivalent, e.g. cum laude) Expertise in quantum mechanics
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, including candidates who are about to complete their degrees) in physics, material science, chemistry or related fields In addition to the above, essential requirements include: Knowledge in semiconductor
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
career in science and/or industry A comprehensive training program, including soft skills, along with an flexible working hours and various opportunities to reconcile work and private life Flexible working
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-based digital twin, which can predict the assortment and sequence of two-dimensional all-DNA motifs required for the engineering desired tessellation patterns at the nanometer scale. We will use available