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Hospital, Copenhagen). The successful candidate will be responsible for designing and implementing the predictive modeling strategy of the project. This includes: Developing machine-learning prediction
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& Machine Learning: Experience in deploying machine learning models and data science workflows in a research context (e.g., cheminformatics, predictive modelling). Design of Experiments (DoE): Knowledge
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI approaches to biological questions Collaborating closely with
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our working and learning environment. We are seeking a highly motivated doctoral researcher to investigate how metabolic programs enable cancer cells to metastasize and colonize distant tissues using
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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learning environment. Your main tasks will be: Establishing models of B and T cell priming in human lymphoid organoids. Performing characterization of organoids and primary samples with flow cytometry and
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/Research/Values-Ethics/Diversity-and-Inclusion.html ), we are committed to sustain and promote an inclusive culture, ensure equal opportunities and value diversity and respect in our working and learning
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responses approximate human behavior. The project involves a collaboration between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain