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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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the Finnish Center of Excellence in Quantum Materials . Your role and goals The research will focus on developing and using machine learning algorithms to discover novel materials and to build generative models
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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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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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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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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
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programming and algorithm development, with proficiency in Python, Perl, C/C++, or Java, and statistical computing using R Demonstrated experience in designing, training, validating, and deploying machine
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the project's personalized treatment algorithms. For further information, please contact Prof. Dr. Antonio del Sol, antonio.delsol [at] uni.lu . Your profile Ph.D. degree in computational biology, bioinformatics
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models to specialized microscopy tasks and develop algorithms that align image level embeddings across modalities (e.g., fluorescence ↔ electron microscopy ↔ brightfield ↔ …). In collaboration with other