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Field
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interpreted using deep learning to estimate tool-to-retina distance and generate accurate three-dimensional navigation commands without relying on conventional 3D reconstruction. Research Objectives
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the Groeblacher Lab we have a vision: we thrive to be an inclusive place where a diverse mix of talented people want to come, to learn, to live their passion and do their best work. We are dedicated to promoting
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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Are you passionate about combining the directed evolution of diverse biomolecules with deep learning approaches and contributing to the development of better (bio)catalysts and drugs? We
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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch
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experts in making better-informed decisions. You’ll gain expertise in 3D deep-learning, semantic technologies, explainable and uncertainty-aware AI, and human-centred evaluation, and test your work in
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research • Deep learning and predictive modeling • Natural language processing and large language models for biomedical data • Drug response prediction and treatment optimization • Biomedical knowledge
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, Robotics, Electrical Engineering, or a closely related field. A strong academic record and solid background in machine learning and deep learning. Ability to develop, understand, and critically evaluate