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project RECLESS (Recycling versus loss in the marine nitrogen cycle: controls, feedbacks, and the impact of expanding low oxygen regions). RECLESS aims to predict how ongoing ocean deoxygenation impacts
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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RISC-V ISA. Your project will be on developing time-predictable RISC-V architectures to support multicore systems as a basis for real-time automotive functionalities. Responsibilities: Conduct research
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potential for exploiting temperature gradients for producing electricity and predict their long-term performance under real operating conditions. The project also includes modeling of heat transfer and
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will join an international team good command of written and spoken English is necessary. As a formal qualification, you must hold a PhD degree (or equivalent) in Allergology, Immunology, Bioinformatics
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talented and motivated Postdoc candidate within the field of Immunoinformatics and prediction of T cell immunogenicity. HLA class II antigen presentation form the cornerstone of T-helper cell immunogenicity