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Exploring in-context learning techniques and novel neural networks architecture to improve adaptability from subject-to-subject and day-to-day Your Profile: Current master studies in biomedical engineering
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relevant option for sustainable and efficient production of hydrogen. In addition to alkaline electrolysis, which has been commercially available for a long time, current research is focusing on PEM (polymer
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develop suitable disaggregation methods in the second part of your thesis and apply them to the researched values. To do this, you will: Conduct a systematic literature review of current demand as
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found using the classical algorithms (from 1) with Quantum Optimization Ansatz (from 3) Your Profile: Enrolled as master student in a university Good background in physics, electrical engineering
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, electrical engineering, chemistry or related fields Know-How in the physics of semiconductors and ideally in spectroscopy of semiconductors Very good grades in bachelor and master courses Very good spoken and
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sources (e.g., solar and wind energy) and the resulting stress on the electrical grid. Building energy systems as large energy consumers (and producers) incorporate several different technologies for load
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current energy technology and policy issues You already have some experience in data analysis and programming with Python or a comparable programming language You are highly motivated and have the ability
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research data management activities, semantic and infrastructural engineering in the field of Material Science, e.g. within NFDI-MatWerk Dissemination of developed tools and concepts within interest groups
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of reduction phenomena using electrical measurements and optical microscopy Preparation of sample cross-sections by sawing and polishing Analysing the structure and composition using electron microscopy and
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tasks will encompass the following areas: Conduct a review of existing literature to understand current load profiles used in non-road mobile machinery. The focus should lie on their operational