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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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writing skills. o Proactive mindset and ability to work in a multidisciplinary and collaborative environment. o Adaptability and openness to learn new tools and methods. Language skills
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skills are an asset Soft Skills: Most importantly, we seek individuals who are curiosity-driven especially interested in interdisciplinary research, eager to continuously learn with critical thinking and
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events (e.g. in the context of ALICES) Participate in the wider academic community through conference and workshop participation Teach and assist in undergraduate and graduate courses Publish research
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education team while being affiliated with the Institute for Teaching and Learning and situated within the Department of Education and Social Work (DESW). The position includes organizing and coteaching
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analysis (R, Python) is an asset. Curiosity, creativity, rigor, willingness to learn, team spirit and collaborative capacity, excellent time and priority management. Fluency in English (written and spoken
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial peptides Perform and support experimental studies across the METAMIC project
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framework to bridge this gap and enable organizations to confidently deploy secure GenAI solutions by evaluating the machine-learning models intrinsically, identifying components of an AI pipeline and their