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industrial experts. Nice to have: Proficiency in programming languages (Python, MATLAB, or similar software) for data analysis/model automation. Prior industrial collaboration or scale-up project experience
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. Desirable criteria Experience working with generative models or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design
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defending the cultural value of knowledge for its own sake. You will also possess computational expertise in data mining and / or analysis, ideally including language processing, and be able to work with an
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Leadership abilities to supervise students and PhD students Teaching experience Very good German and English language skills. Very good mathematical understanding Very good programming skills Strong ability
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or in national administrative or legal practice are of advantage. Excellent knowledge of German and very good knowledge of English (C1), knowledge of another foreign language is an advantage. You exhibit
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Participate in meetings to discuss the AQPU plan together with the team Help your peers in solving their challenges in reaching the milestones including the supervision of BSc theses and possibly of PhD and MSc
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with IoT, edge computing, and cloud integration in rural or resource-constrained settings. Proficiency in MATLAB/Simulink and AI/ML tools like TensorFlow or PyTorch. Excellent programming skills (e.g
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. Research competence & initiative demonstrated through international publications in peer relevant media. Excellent command of English, both written and spoken You are goal-oriented and have a high motivation
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related discipline and have begun to establish a strong research profile, evidenced by a strong, well-cited publication record. Proficient in Python, R, BASH and/or other relevant programming languages, you
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly