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visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Exceptional collaborative abilities Self-motivated individual with ability to work independently
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a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model
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of cohort analysis, prediction modelling, or machine learning techniques • Good knowledge about pancreatic cancer epidemiology • Excel in R or SAS • Good publication records Priority will be given
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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education into teaching is also required. documented ability to teach in Swedish or English. In addition to academic qualifications, teaching and training experience from other contexts may also be considered
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will
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machine learning approaches, particularly neural networks, and experience with workflow management systems (e.g., Snakemake, Nextflow). Knowledge of transcriptomics and alternative splicing analysis
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agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues