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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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of Malignant Hematology/Medical Oncology) and UPMC Hillman Cancer Center. This position is supported by an NIH/NIGMS R35 grant and intramural funding. The successful candidate will engage in bioinformatics
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of the IML group within prediction of T cell immunogenicity and immunoinformatics in general. You will work with nearby bioinformatics, postdocs and PhD students working on other projects within the group and
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responsibilities include bioinformatics analyses with a strong focus on single-cell RNA sequencing and next-generation single-cell barcode lineage tracing, as well as the interpretation of complex biological
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: applicants should hold a PhD degree in quantitative science such like bioinformatics, physics, computer science or a comparable field proved outstanding skills in computer science, physics, mathematics, or a
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Engage in teaching and supervision as required Expectations of qualifications PhD in Bioinformatics, Computer Science, Physics, Engineering, Bio-engineering, or equivalent Excellent track record in
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to illness, parental leave, clinical service, trade union duties or similar circumstances. The PhD degree should be in a computational or life science field, such as bioinformatics, ecology, mathematics
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. For more information regarding the position or details of the project, please contact Chloé B. Steen (chloebs@uio.no ). Qualifications Completed PhD in computational biology, bioinformatics, computer science
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presentations at international conferences Our requirements Completed PhD in Biology, Biotechnology, Bioinformatics, Immunology, or related field Strong interest in computational science and AI (prior formal
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experience and enhanced potential to receive an ERC Starting Grant in the future. Open to both PhD (natural sciences) and MD (medical sciences) holders. From a variety of academic backgrounds: molecular