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Field
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, physics, or data science. They should have strong analytical skills in the context of machine learning and/or numerical mathematics, as well as an excellent command of a programming language, preferably
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data, log-trace data from learning platforms, and panel data. Relevant areas of expertise include longitudinal data analysis, psychometrics, learning analytics, and machine learning. We are particularly
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seek a highly motivated scientist with a MSc/Diploma in life sciences (e.g. biochemist, nutritionist, environmental sciences) or analytical chemistry or comparable. The applicant should have interest in
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laboratory environment is desirable Knowledge of data analysis and processing using common software tools You enjoy learning new practical skills, approach unfamiliar topics with curiosity and structure, and
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software tools You enjoy learning new practical skills, approach unfamiliar topics with curiosity and structure, and quickly grasp them through analytical thinking. What we offer A unique combination of
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(attribution and verification) Implementation and application of various machine learning methods/concepts, such as: One/Binary-Class Classification: LLM-based (e.g., zero/few shot learning, fine-tuning
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and evaluation of innovative data-driven and Machine Learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop novel optimization
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interfacial chemistry on the atomic-scale is as yet unresolved, owing to the lack of sufficiently suitable analytical capabilities. In this project, the PhD candidate (m/f/x) will employ atom probe tomography
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learning approaches. A central aspect of this project is the formation of a complex sorption layer—known as the eco-corona—on the nanoparticles and its influence on pollutant sorption. We are seeking to hire
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. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g