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international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a position as a system developer at
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An appointment as Associate Senior Lecturer/Assistant Professor (tenure-track) in Plant Physiology with focus on plant embryo development is available at the Department of Forest Genetics and Plant
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systems. Our work spans the full development cycle, from quantum device design and nanofabrication to low-temperature characterization and quantum measurements. You will join the Quantum Computing Group
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(especially silicon-based) and cathode applications. In this role, you will: Develop new binder systems for electrode processing. Synthesize and modify binders for innovative manufacturing processes. Balance
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is to combine multi-gene control technology and computer algorithms to develop a foundational discovery platform for future cell programming applications. This position involves both experimental and
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environments. This research direction demands developing novel techniques and algorithms that can enable effectively integrating sensorimotor information with learning algorithms, and, at the same time, leverage
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the foundations of computer science. Subject Description The research subject focuses on an integrated development of network architectures, resource efficient algorithms, and programming paradigms for enabling
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. The research team focuses on developing novel methods to extract knowledge from data, modeling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but
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experience in experimental particle physics and data analysis Prior experience with machine learning tools Prior experience in developing algorithms such as particle identification, specific final state event
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC