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trials and deep learning-based modeling of multimodal biomedical data. We are seeking two highly motivated scientists with expertise in Causal Inference / Statistics / Deep Learning / Bioinformatics
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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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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 2 months ago
for 4 years Software Developer / Computer Scientist (m/f/d) specialised in AI & Machine Learning, Ref. 421 to support the further development and commissioning of the Real-Time Control (RTC) system
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Basic knowledge in at least one of the following areas: integrated photonics, optical communications, machine learning/neural networks, or numerical simulation of optical systems Programming experience in
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 2 months ago
and experience Experience with 3D parametric body models (i.e. SMPL/MANO/ATLAS/VAREN/etc.) Experience in Computational Geometry, Computer Vision and Deep Learning Accepted publications in one
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer
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machines Machine Culture Ongoing work on social reinforcement learning and evolutionary optimization of social strategies Our aim is to advance the scientific knowledge of human-AI systems by understanding
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Learning commencing on April 1st, 2027. The Junior Professorship in "Probabilistic Methods in Machine Learning" is at the interface of applied mathematics, statistics, and computer science. It is dedicated
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addition, the candidate should demonstrate research experience in one or more of the following areas: machine learning (e.g. deep learning) for process models, or the optimization of bioprocess development
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for future energy technologies. Be part of change Conducting literature reviews and evaluating state-of-the-art approaches in Machine Learning and Deep Learning Performing data preprocessing, feature