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neuroscience to gain a deeper understanding of the principles and algorithms underlying the computations of visual systems and enabling “robust vision.” The aim is to investigate the fundamental principles and
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machine learning algorithms Strong communication skills and ability to work in interdisciplinary teams Fluency in spoken and written English We offer: A dynamic and interactive research environment as
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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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), with a focus on machine learning, deep learning, or AI. Solid mathematical, algorithmic, or physics background, distinct analytical skills. Very good programming (Python, C++) and computer (Linux
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manipulation detection. Another focus is the development of algorithms for the areas of virtual product development, intelligent actuator-sensor systems and audio for the automotive sector. There are currently
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experimental molecular biology and data analysis. Doctoral candidates can specialize in genomic and molecular biology techniques, as well as in algorithms, statistics, and artificial intelligence for molecular
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physicians. support LIT scientists in processing, analysis interpretation and publication of transcriptomic, epigenomic and other high-throughput data. improve NGS data analysis algorithms and design novel
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: Development of machine learning algorithms for the localisation of seismic sources (e.g., on 2D grid maps) Analysis and preprocessing of large DAS datasets Use of synthetic training data from seismic