121 phd-computer-science-"IMPRS-ML"-"IMPRS-ML" Fellowship positions at Nature Careers
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significant practical experience in EMI shielding, Mxene and wood composites research. The candidate will have a PhD in Physics, Chemistry, Materials Science or a related discipline, or equivalent experience
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for the BISCA distinct approach. As one of two Research Fellows in the Institute, we anticipate recruiting complementary skills from either a STEM background, or a social sciences/art and humanities background
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. Qualifications Please include a CV, cover letter, and contact information for 3 references in the application. A PhD, PhD-MD or doctoral equivalent in Cancer Biology, Epigenetics/Genetics, Molecular Biology
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research independence. Candidates are expected to have no more than four years of post-PhD research experience. Extensions to this time period will be considered in circumstances that support our commitment
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. Strong written and verbal skills in English are essential. PhDs from Cell Biology, Bioengineering, Biophysics, Biochemistry, Physiology, Immunology, Pharmaceutical Sciences, Pathology and Molecular Biology
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the final stages of PhD completion, in a relevant biomedical science (including but not limited to: immunology, molecular biology, cell biology, biochemistry) Experience in molecular biology, mammalian cell
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molecular biology and rodent models · Have experience with epigenetic techniques Your qualifications should include: · PhD in Molecular, Cellular or Developmental Biology. · Strong
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generated. Required Qualifications PhD in neuroscience, developmental biology, or related field Strong expertise in mouse neurodevelopment (mandatory) Skilled in histology, immunofluorescence, and molecular
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EMBL is Europe’s life sciences laboratory – an intergovernmental organisation with more than 110 independent research groups and service teams covering the spectrum of molecular biology. It operates
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selected candidates. This is a high-impact role with the potential to shape the future of rare disease research. Join us to push the boundaries of what’s possible in computational biology and personalised