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Field
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profile: Team spirit, scientific curiosity, and eagerness to learn and make fundamental discoveries Master's degree in the life sciences (M.Sc.) or an equivalent qualification Very good communication and
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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. You are interested in learning about new, data-based options at the interface of qualitative and quantitative research (including Big Data) and, based on this, in formulating policy recommendations and
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Develop new machine learning
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with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team orientation Excellent spoken and
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sector pay agreement (TVöD EG 13; after interim evaluation TVöD EG 14). The successful candidate will have the opportunity to pursue a habilitation and to teach at the International Max Planck Research
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methods are a plus High degree of motivation, willingness to learn and team spirit Strong sense of responsibility, organisational skills, high reliability and flexibility Good written and oral
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22.12.2025, Academic staff We are an interdisciplinary team at the Chair of Safety, Performance and Reliability for Learning Systems, and we are looking for exceptional PhD candidates to join our