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Incorporating reinforcement learning strategies to guide model optimization, with a focus on minimizing immune responses and improving therapeutic viability Your Profile: Completed Master/PhD Degree in the field
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strong background in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python
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for image-based modelling Your profile PhD in physics, materials science, computer science, applied mathematics or a related field strong background in image processing and analysis, including deep learning
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, physics, mathematics or a related field Programming skills in Python required (Numpy, Pandas, Scikit-learn, Pytorch) Hands-on experience in Deep Learning Demonstrated interest in working with biological
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Your Job: In this position, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting
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mathematics or a related field strong background in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong
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to advancing machine learning in biomedicine. The Program focuses on developing and applying cutting-edge AI approaches to address key challenges in molecular biology, clinical research, and translational
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cutting-edge technologies — from single-cell multi-omics and deep learning to live-cell imaging and stem-cell–based organoid systems — to predict, observe, and manipulate epigenetic processes. Our lab and
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engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 30 Jan 2026 - 23:59 (Europe/Brussels) Type of Contract Temporary Job Status Full-time
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engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Germany Application Deadline 30 Jan 2026 - 23:59 (Europe/Brussels) Type of Contract Temporary Job Status Full-time