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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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application! This position is in a lab with an interest in many aspects of theoretical ecology (coexistence, niche theory, ecological networks, metacommunities, eco-evolutionary dynamics, and more), where you
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laboratory work and data analysis? Then this position may be right for you. While the focus will be on your research and personal development as a researcher, the position also provides opportunities
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spaces of AI models will be analyzed in their facts using quantitative methods from data science and AI, results of which then be investigated using qualitative methods and theory of critical studies
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of adsorption and biodegradation, little is currently known about their interaction. This project aims to fill that knowledge gap by: Operating laboratory-scale GAC filters Sampling from full-scale filters
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the intersection of control theory, robotics, artificial intelligence, and signal processing, and is a key enabler of future intelligent infrastructure.This project will focus on the development of novel control
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of robots to interact with the surrounding environment and humans during the execution of specific tasks. Project description The vision of RAI is aiming in closing the gap from theory to real life, while
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of robots to interact with the surrounding environment and humans during the execution of specific tasks. Project description The vision of RAI is aiming in closing the gap from theory to real life, while
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disciplines Excellent written and spoken English Experience with optimal control theory and methods Contract terms The position is regarded as a full-time employment with a competitive monthly salary and full
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Exciting Opportunity in Sustainable Energy Research: Join Us in Advancing Hydrogen Storage Technology! Hydrogen is a critical energy carrier for future sustainable systems. One issue using hydrogen