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Faculty Position — Research Scientist in Cardiology, Cardiac Regeneration and Translational Cardiova
Initiative: Developing platform products that enable prediction and prevention of diseases through biological signatures, real-time sensing technologies, and algorithmic approaches. Bold. Forward
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, neurodevelopmental outcomes, inflammation, and energy metabolism. Job Responsibilities: Independently perform basic and advanced level statistical analysis, algorithm implementation, programming from a variety of
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collaboration by proposing an original hybrid rule-driven/data driven approach to artificial intelligence and by studying efficient optimization algorithms. The team focus on robotic applications like environment
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scientific programming and algorithms is desirable Experience working in a research environment a plus General languages (preferred): C++, Python, MATLAB CUDA (strongly preferred) Machine learning knowledge
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increasingly shapes biomedical research and healthcare decision-making, we also value candidates who can help students critically understand how algorithmic systems affect equity, access, bias, and real-world
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imaging, and the application of basic image analysis algorithms utilising our state-of-the-art instrumentation (Ventana Discovery Ultra, Akoya PhenoImager, Akoya PhenoCycler, Visiopharm, 10x Xenium). You
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functional genomics (CRISPRi/a, Perturb seq, combinatorial screens), single cell and spatial omics, metabolomics, and immunopeptidomics. The successful candidate will pioneer assay-algorithm co design
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optimize algorithms for analyzing behavior data. Build or implement existing scripts to temporally align data across multiple modalities. Coordinate efforts with DNB researchers and established vendors (Med
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Software Engineer - Image Quantification and Artificial Intelligence (IQAI), Department of Radiology
, and ensure reproducibility. Build user-friendly interfaces and APIs that enable radiologists and researchers to interact with complex image analysis algorithms. Translate computer vision and deep
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computational methods and algorithms for genomic sequencing data analysis, particularly in the context of genome assembly. This is an exciting opportunity to develop novel computational approaches for microbial