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In this position, you will join our Simulation and Data Lab for AI and Machine Learning for Remote Sensing . The lab advances interdisciplinary research and operational services by combining
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evaluate statistical and machine learning models - Publish results in peer-reviewed journals Desired Qualifications - Master’s degree in statistics, mathematics, data science, bioinformatics, physics
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 15 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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longitudinal within-person models, discrete-time survival analysis, and/or explainable machine learning. Each of the three work packages is designed to result in one publication, together constituting a
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numerical simulations and machine learning in order to better understand and characterize active matter systems. A possible direction is to use physics-informed machine learning techniques to connect
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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, especially in quantitative subjects • Strong Python skills and experience with deep learning frameworks, preferably PyTorch • Solid foundations in machine learning, statistics, linear algebra, and model