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Field
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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plate array microscope for simultaneous time-lapse video microscopy, enabling high-throughput single-cell analyses of rapidly migrating cells. You will be responsible for Developing new machine learning
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science and information science techniques. Several areas of computer science and mathematics play important roles: data management and engineering, machine learning and data analytics, signal and image
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Degree Doctor of Philosophy (PhD) / Doctor rerum naturalium (Dr rer nat) Course location Hannover In cooperation with Twincore - Centre for Experimental and Clinical Infection Research, University
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, informatics, physics or a related field strong expertise in machine learning strong interest in high performance computing on CPUs and GPUs proficiency in Fortran, Python, shell scripting proficiency with Linux
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Degree Doctor of Philosophy (PhD) / Doctor rerum naturalium (Dr rer nat) Course location Hannover In cooperation with Twincore - Centre for Experimental and Clinical Infection Research, University
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Degree PhD Learning Sciences (alternatively Dr phil, Dr rer nat, Dr med; not recommended for international students) Course location München Teaching language English Languages Courses are held
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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data. Develop and apply machine learning models to estimate uncertainty in climate impact statements. Analyse spatial and temporal patterns and trends in climate-extreme impacts. Cross-validate
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(4DSTEM). This approach will combine three-dimensional charge distribution data, generated through atomistic simulations, with machine-learning-driven modelling to guide and refine the phase reconstruction