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Job description:Title: DC13, PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products – Development of a serious game to support the
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Deutsches Zentrum für Luft - und Raumfahrt (DLR) | Koln, Nordrhein Westfalen | Germany | 3 months ago
4 Apr 2024 Job Information Organisation/Company Deutsches Zentrum für Luft - und Raumfahrt (DLR) Research Field Technology Engineering Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Germany Application Deadline 7 Aug 2024 - 00:00 (UTC) Type of Contract To be...
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is a fast-developing area of machine learning, holding the promise to improve artificial reasoning and intelligence. Causal models can provide reliable formal tools to agents and policy-makers
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39-21272 Date of filling: As soon as possible Application deadline: 31.12.9999 The Neural Data Science Lab (Prof. Ecker) and the Machine Learning Lab (Prof. Sinz) of the Institute of Computer Science
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duties at the Department). About the project/work tasks Causality is a fast-developing area of machine learning, holding the promise to improve artificial reasoning and intelligence. Causal models can
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degree) in human-computer interaction, industrial design, computer science or similar with a genuine interest in pursuing a PhD. A team player able to work in a dynamic, interdisciplinary environment
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scattering and runs a specialized subgroup on machine learning (artificial intelligence and deep learning) for the analysis and prediction of experimental scattering data. Currently, there are several options
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to machine learning and artificial intelligence is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills. Applicants must be
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. Familiarity with research relating to machine learning and artificial intelligence is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative
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to data-driven (machine-learned) representations. In particular, we are interested in the joint applicability of such models and to what extent simpler models (possibly based on machine learning) can be