13 computational-linguistics Postdoctoral positions at Technical University of Munich in Germany
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requires experience in cognitive processes underlying food choice, context effects, experimental research, quantitative analysis, computational modeling, and eye tracking. Applications must be submitted by
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: generative models for explainable patient representations, multimodal data fusion, vision-language models, and anomaly detection. Also includes student supervision and EU project coordination. Requirements
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multimodal vision-language models for prompt-based 3D medical image segmentation Work with large-scale clinical CT datasets and scalable deep learning pipelines Validate models in close collaboration with
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mathematics, physics). For Postdoc applicants: Excellent track record in engineering or computer science. Fluency in spoken and written English is required. Proficient in at least one programming language, e.g
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, justice, technology, or on other forms of related inequality is desired. Fluency in English and one additional language is required. Applicants must have substantial fluency in the second language, such
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skills in Python, Java, C++, etc. A solid foundation in generative AI, machine learning, and related areas. An Interest in eye-tracking technology, Computer Vision, Speech/ Language Processing, VR, and AR
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framework for AI in gynecological oncology. We integrate symbolic knowledge representation (Ontologies/Knowledge Graphs) with Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to create
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% international students in the MA program. The visual computing lab embraces its diverse culture, and is proud to host PhD students from over 10 different countries. Our lab language is English. The position is
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parameters are tuned so that the over-approximation of the computed reachable set is small enough to verify a given specification. We will demonstrate our approach not only on ARCH benchmarks, but also on
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systems in East Africa, and in the subtropics in Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We conduct