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nanotechnology and electrical sensing investigations (with 2D nanostructures), with an established track record of success in high-quality original research. Applicants should have a solid grounding in different
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preferred Good understanding of cancer biology, immunology, cell biology, and molecular biology is required Proven track record of peer-reviewed publications Excellent spoken and written communication skills
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-ecological modeling (24 months, 100%), you will fill a central position to reach important objectives of the project. Equal opportunity is an important part of our personnel policy. We would therefore strongly
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project, we will use mantle cell lymphoma (MCL), an aggressive and incurable B-cell lymphoma, as a model to explore how epigenetic modifications contribute to tumor resistance. Our main objectives are: 1
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marine biology, zoology, and/or biochemistry. • Strong track record in managing projects and undertaking scientific studies to completion. • Previous track record working in the research field of fisheries
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discipline (e.g. Medical Informatics, Computer Science, Data Science, Computational Social Science, Epidemiology, Biostatistics, etc.) Strong research track record, programming, and implementation skills
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scientist with a strong biological background to lead experimental work at the nano–bio interface. The ideal candidate brings a track record evidenced by a solid publication record. Our research centers
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Postdoctoral Researcher (Physicist, Chemist or Material Scientist( (all genders) for a Tenure Tra...
, Chemist or Material Scientist) (all genders) for a Tenure Track Position Posting ID: 25.89-6260 The Target Laboratory is one of the scientific-technical infrastructure departments of GSI and FAIR and a
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, which also encompasses single-cell resolution spatial transcriptomics of human plaques to precisely identify and characterize disease-relevant smooth muscle cell phenotypes, a parallel track of organoid
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structured biomedical data; Strong quantitative and analytical skills applied to observational or clinical datasets, and familiarity with techniques for representation learning and sequence modeling; A track