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data types (transcriptomics, proteomics, imaging). Knowledge on AlphaFold for models in structural protein analysis/proteomics AI/ML Applications: Applying machine learning or AI to predict gene function
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, and manipulation of high-dimensional imaging and mass spectrometry data Experience in designing and maintaining reproducible and scalable analysis workflows Solid foundation in statistics and machine
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(transcriptomics, proteomics, imaging). Knowledge on AlphaFold for models in structural protein analysis/proteomics AI/ML Applications: Applying machine learning or AI to predict gene function or discover functional
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registries and biobanks. The applicant is expected to have a strong computational focus on innovative development and application of novel data-driven methods relying on machine learning, artificial
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: Experience combining proteomics with genomic/transcriptomic data Specialized knowledge: Understanding of peptide-spectrum matching, FDR estimation, protein inference AI/ML proficiency: Experience with machine
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spatial mass spectrometry. Experience with single-cell omics is also an advantage. Advanced biostatistics and machine learning, such as multivariate analysis, regularization, deep learning, or network
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methods relying on machine learning, artificial intelligence, or other computational techniques. Duties The position includes research, teaching and administration. Duties includes conducting research
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on machine learning, artificial intelligence, or other computational techniques. Main responsibilities Research and in addition some teaching and supervision. Qualification requirements In order to qualify
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advanced biostatistics/machine learning analyses, but also with other types of analysis. The work involves supporting Swedish researchers under a “user fee-based” support model. The projects will differ in
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, e.g git. Experience of Linux/UNIX-terminal. Excellent communication- and collaboration skills. You speak and write English fluently. Meriting Experience of HPC or corresponding computer systems