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language models (LLMs) Proficiency in Python programming and confident use of Unix/Linux environments; ideally experience with version control systems (e.g., Git) Interest in or experience with semantic web
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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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programs (Microsoft Office), SAP knowledge is an advantage A confident appearance as well as a courteous manner with guests and visitors is a matter of course for you Your communication skills in German and
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scientific journals and conferences Your Profile: A doctoral degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural language
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defossilization of the economic system. CO2 capture from ambient air and its storage represents an important technical solution in this context. In your master thesis you will develop approaches based on Natural Language
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programming (e.g. Python, Matlab) is desirable Interests in issues about decarbonization and multi-energy systems Excellent ability for cooperative collaboration Good knowledge of written and spoken English
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Deadline 19 Jan 2038 - 03:14 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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Deadline 19 Jan 2038 - 03:14 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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team of neuroscientists, computer scientists and clinicians Your Profile: Excellent university degree (Master) in neuroscience or a related field Solid understanding of statistics and programming (Python
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assemblies, including the evaluation of measurement data Proven laboratory experience and programming proficiency (Python or a comparable language) Ability to work independently and self-reliantly, while