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implementation of the new HClimRep foundation model, Assist the HClimRep team with testing and improving the HClimRep model, Develop a concept of software package of hydrological downscaling based on the HClimRep
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considering the potential to apply these methods to different domains. Specifically, you will: Develop, implement and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL
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the potential to apply these methods to different domains. Specifically, you will: Develop, implement, and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL) for scientific and large
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Continuous mentoring from your scientific supervisor 30 days of annual leave and provision for days off between public holidays and weekends (e.g. between Christmas and New Year) Opportunity to develop your
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Your Job: However, one of the main hurdles related to AEMWE technology is to develop low-cost and robust catalysts that can withstand the harsh reaction environment without compromising
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protocols, and systems within the project. Algorithm Development: Using Python programming language to develop the algorithm and implement the controller in the cloud environment. Testing and Release: Conduct
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for your thesis with a forward-looking theme The chance to independently prepare and work on your tasks A large research campus with green spaces, offering the best possible means for networking with
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. Identify the techno-economic parameters as well as the land and water requirements for these protein sources. You have the opportunity to develop different scenarios from your results in order to model and
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. In order to understand these phenomena in detail and to develop concepts for increasing the operation lifetime, fundamental physicochemical investigations are necessary. In this project, the behaviour
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membranes. You will develop theoretical models and employ computational methods to describe the coupling between ionic nanofluidics, electric double-layer charging, and interfacial reactivity in electrolyte