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                at cell membranes; Apply machine-learning models trained on simulation data to study how lipid composition and genetic variation influence the conformational and phase properties of membrane-associated 
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                is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of 
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                professional development opportunities and strive to meet each individual’s development and well-being goals as much as possible. As an associate researcher with expertise in the field of machine learning within 
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                multiphase flow behavior. The project also involves applying machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid 
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                funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing new machine learning methods for multimodal data and 
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                applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery 
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                look forward to receiving your application! At the intersection between AI and single atoms. Your work assignments We are looking for a PhD student with a background in machine and deep learning with 
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                statistical and algorithmic methods to analyze large amounts of simulation data, models that explain how and why an autonomously controlled machine fails or underperforms, and methods to recognize simulation 
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                statistical and algorithmic methods to analyze large amounts of simulation data, models that explain how and why an autonomously controlled machine fails or underperforms, and methods to recognize simulation 
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                , contribute to a better world. We look forward to receiving your application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and