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- Adam Mickiewicz University, Poznań
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the project “Modeling of optically excited states in periodic and molecular systems with the self-consistent Green's function methods” financed by the NAWA Returns grant awarded by Polish National Agency
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Offer Description The goal of the Opus 27 project “Ab-initio Green’s Function Methods for Describing Magnetic Compounds with Strong Spin-Orbit Coupling” (Principal Investigator: Dominika Zgid), funded by
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as amended) and who meet the following requirements: 1. PhD in physical sciences. 2. Fulfilled formal requirements regarding the date of obtaining the doctoral degree in accordance with the regulations
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or equivalent Specific Requirements Requirements for the candidate: PhD in biology, biochemistry, or related life science field, for not more than 7 years before the position starts. Hands-on experience in basic
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methods and potential application of machine learning techniques. Good command of English (B2/C1). The candidate will be responsible for carrying out tasks related to the analysis of experimental data
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– the regulations regarding SONATA BIS 14 - Annex to the NCN Council Resolution No. 59/2024 of June 6, 2024. An ideal candidate will: hold a PhD degree in biotechnology, molecular biology, biochemistry, biophysics
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of the microbiota. Specific tasks will depend on applicants experience and interests. Specific Requirements Requirements: Hold a PhD degree in Biology, Biotechnology, Medicine or an equivalent field
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mechanisms responsible for the expansion and function of human beta cells that produce insulin. Technologies you will work with: The project uses state-of-the-art research methods: • Culture and
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the requirements specified in Article 113 of the Act of July 20, 2018, Law on Higher Education and Science (Journal of Laws 2024, item 1571 as amended), and who also meet the following requirements: PhD
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: designing and implementing methods for generating synthetic data aimed at diagnosing limitations, biases, and errors in AI models; developing and evaluating strategies for exploring the decision space of AI