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Internal Number: 7071018 Postdoc Research Associate Job Identification: 20071 Posting Date: 04/09/2026, 08:03 PM Job Schedule: Full time Locations: 101 Bagby Avenue, Waco, TX, 76706, US Degree Level: Job
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, microbes will be identified that affect fermentation outcomes. Building on previous work from the host group (doi:10.1016/j.chom.2023.05.024; doi:10.1093/nar/gkaf1388), we will develop machine learning tools
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the use of machine learning and AI approaches • Integration of proteomics with genetic data via MR, coloc and FUSION to identify causal and druggable targets Requirements • The successful applicant will
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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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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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humanities disciplines. Familiarity with contemporary AI systems (e.g., machine learning, generative models) at a conceptual or applied level. Experience with qualitative or mixed research methods (e.g
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Project description Third-cycle subject: Biotechnology The project aims to develop probabilistic deep learning models