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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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Your Job: This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining
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. Information on employment as a PhD student at Forschungszentrum Jülich can be found here http://www.fz-juelich.de/gp/Careers_Docs The position is limited to three years, with a possible one-year extension. Pay
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at: www.fz-juelich.de/gp/Careers_Docs Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We
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agreement can be found on the BMI website (pay scale table on page 69 and following of the PDF download): https://go.fzj.de/bmi.tvoed Further Information: hds-lee@fz-juelich.de We welcome applications from
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Infrastructure? No Offer Description Work group: IAS-6 - Theoretical Neuroscience Area of research: PHD Thesis Job description: Your Job: This PhD project bridges between classical analytical methods and modern AI
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for a highly motivated PhD candidate to join our world-leading research program in Earth System modelling and improving Earth System Modeling by better merging of measurement data and model simulations
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information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We welcome applications from people with diverse
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/bmi.tvoed.entgelt Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at www.fz-juelich.de/en/careers/phd We welcome applications from people with
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, computer science, or a related field Proficiency in at least one programming language (Python, C++, …) Experience in neuroscience is an advantage Good analytical skills with a sound understanding of data evaluation