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Application deadline Applications must be received by 15 August. Tuition fees per semester in EUR Varied Combined Master's degree / PhD programme No Joint degree / double degree programme Yes Description
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | about 24 hours ago
of computational load for such a development. In the frame of a third party funded research project, we are looking for a PhD Student to work on and develop breeding strategies to mine Winter-wheat accessions stored
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Program for Collaborative Doctoral Projects, please visit: https://go.fzj.de/Collaborative-Doctoral-Projects . We are offering a Collaborative Doctoral Project (PhD Position) – Computationally-Enabled
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the application of machine learning (ML) methods or large language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g
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, giving rise to complex lesions. GSI is working on the quantification of such complex lesions in particular in dependence on radiation type. Within this program, the GSI biophysics department offers a PhD
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two part-time positions (65%) as Doctoral Researcher (PhD Position, m/f/d) in the field of Neurobiology In accordance with § 2 of the WissZeitVG and § 72 HessHG, the positions are to be filled on a
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written and spoken English skills High degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very
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implementation on IBM’s ibm_cleveland quantum computer by reproducing recently published benchmark QM/MM simulations [2] Apply the developed code to simulate proton transport in vesicular glutamate transporters
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partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a
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computer science, bioinformatics or related fields Solid understanding of machine and deep learning and relevant frameworks (e.g. Pytorch or Tensorflow, Keras, scikit-learn, OpenCV) Proficiency in Python, Linux and