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have: good knowledge of quantum mechanics knowledge of electromagnetism and solid-state physics experience with scientific programming with e.g. Python, Matlab, Julia experience with writing a scientific
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related field Documented metagenomics experience Experience with writing and running data analysis scripts in a command-line interface Solid programming experience in Python Experience with scripting and
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, hysteresis, oscillatory behaviour and support dependencies. Compare cluster behaviour across different characterization techniques (TEM, STM, TPD). Integrate findings to map the relationship between cluster
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twins, energy islands, electrolyzers, and machine learning. Our team of 25 members (link ) from 13 different nationalities values diversity and includes experts in a broad range of scientific disciplines
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to dimension the reserves for balancing these power systems with manual and automatic reserves such as mFRR, aFRR and FCR. This PhD project will model different balancing principles including MARI and PICASSO
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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that provides a sufficient degree of background in computer science, artificial intelligence, mathematics and/or data science. Fluency in English and Python are required. Experience working with real-world
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; Synopsys or Mentor Graphics. Strong programming skills: Python and C/C++. Experience in hardware accelerators, in-memory processing, or SoC integration. Strong analytical and problem-solving skills, with
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Nordisk. The project will explore the correlation between gene dosage, stability and productivity. Further, different approaches aimed at stabilizing genetic constructs, such as genome integrations
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, satellite altimetry, ice flow maps and terminus positions and other relevant data to constrain numerical model to simulate 1900-present and future (present-2100) ice flow changes under different UN IPCC