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techniques, mouse models, human specimens, and bioinformatics. We are looking for applicants with wet lab immunology research experience driving the successful completion of projects and authoring peer
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Computer Science, Biomedical Engineering, Bioinformatics, or a related field. Required: Strong understanding of machine learning (ML) and deep learning (DL) methods, with hands-on experience in model development and
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imaging is also required. However, experience with bioinformatics or computational tools for biology is highly recommended but not required. This position provides an opportunity to lead and run experiments
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modeling. Experience in cell culture, molecular cloning, and bioinformatics analyses is required. Proficiency in statistics and programming are highly meriting, especially in gene regulatory networks
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publication: computational biology/bioinformatics, cheminformatics, analytical chemistry/mass spectrometry/metabolomics, or machine learning/computer science.Term of appointment is based on rank. Positions
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
partners (e.g., on various environmental factors and communities of other size classes). Key tasks will involve: Perform bioinformatic analysis of microbial molecular data (metabarcoding, metagenomes
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corresponding scientific project management is desirable Confident in independent bioinformatic and statistical analysis of high-throughput sequencing data Experience in working with biofilms is desirable
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and prevent preterm birth. The lab uses innovative and cutting edge experimentation and relies heavily on bioinformatics and biomechanical systems engineering. Job Description Primary Duties
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written and oral communication skills and experience or interest in bioinformatics and in vivo work (holding a personal licence) would be advantageous. What We Offer As an employer, we genuinely care about
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a non-federal source. The position involves applying bioinformatic and statistical tools to existing multidimensional datasets, including metagenomic, transcriptomic, proteomic, and metabolomic