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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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, and in-depth data analysis? We're looking for a fast-learning individual with strong transferable research skills to join our Digital Machining team as a Project Engineer In this role, you'll be
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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interdisciplinary team with clinicians and engineers; You have strong programming skills in Python; You have knowledge of medical image processing, and machine learning and deep learning techniques; Written and
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 10 days ago
. Build statistical and machine-learning models to infer RNA regulatory networks and developmental splicing programs, translating results into experimentally testable hypotheses. Your profile Master's
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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of industrial processes. In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model
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illumination variations, which introduce non-stationary shifts and degrade the performance of conventional models. The project proposes the use of hypernetworks to dynamically adapt the parameters of the gaze