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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Research Description The project aims at combining three primary data sets (JWST NEXUS spectroscopy, Euclid Deep Field North
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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Learning (ML), Deep Learning (DL) particularly in Natural Language Processing (NLP) and Computer Vision (CV) - strong record of publications - Excellent communication skills and ability to work in a fast
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strong technical expertise in deep learning, such as models for image segmentation, classification, multi-modal processing, foundation models, or agentic frameworks. An extensive background in computer
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Infrastructure? No Offer Description Post-doc Position (M/F) at Poznan University of Technology, Poland Project financed by National Science Centre, Poland: call SONATA 21 Project name: Deep insight into biofilm
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scattering and direct dark matter detection, using advanced cryogenic detectors. The MBI hosts a research group dedicated to Machine Learning in Particle Physics. Qualifications: PhD in Particle Physics
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learning to understand and predict the behavior of cardiovascular tissues and organs across scales. You will work closely with PhD students, MSc students, and international VITAL partners. This is a full
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their valuable contributions and deep connection to country and pay respect to Elders past and present. Advance internationally recognised Antarctic climate and palaeoclimate research Be part of nationally funded
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(e.g. use of OpenRouter, vLLM). Experience training LLMs and deep learning in general is a plus; good communication skills in oral and written English. Contributions to OpenML or other dissemination
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interpretability and enable causal and counterfactual (“what-if”) analyses. Your work will sit at the intersection of deep learning, genetic epidemiology, and public health, with opportunities to lead methodological