University of Oxford Featured PhD Programmes
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Statistics (cellular biology) PhD Projects, Programs & Scholarships

We have 9 Statistics (cellular biology) PhD Projects, Programs & Scholarships

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We have 9 Statistics (cellular biology) PhD Projects, Programs & Scholarships

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Fully-Funded PhD Positions in Biology, Computer Science, Data Science & Scientific Computing, Mathematics, Neuroscience, Physics and Chemistry

The Institute of Science and Technology Austria (IST Austria) is looking for highly qualified candidates with bachelor's or master's degrees to apply for the IST Austria PhD program. Read more

Single Cell Biology of Hematopoietic Stem- and Progenitor Cells in Blood Cancer and Ageing

The focus of the Nerlov laboratory is combining single cell biology (single cell RNAseq, ATACseq and functional analysis) with advanced mouse genetics to study hematopoietic stem– and progenitor cells in normal development and during ageing. Read more

Single Cell Biology of Hematopoietic Stem- and Progenitor Cells in Blood Cancer and Ageing

The focus of the Nerlov laboratory is combining single cell biology (single cell RNAseq, ATACseq and functional analysis) with advanced mouse genetics to study hematopoietic stem– and progenitor cells in normal development and during ageing. Read more

Investigating Noise in Ageing Cellular Power Stations

PhD Project. Imperial College Mathematics. Student Background. Theoretical Physics, Mathematics/Statistics, Electrical Engineering (Biological knowledge not required) or Quantitative Biology with an interest in experiment. Read more

Synthetic-evolution: linking genomics to biophysics to understand the rules of life.

  Research Group: Institute of Quantitative Biology, Biochemistry and Biotechnology
The genome of every organism encodes genes and regulatory elements required for building and maintaining cells in homeostasis. However, it is still unknown what is the minimal set of genomic elements necessary and sufficient for cellular life. Read more

Scalable probabilistic modelling for high-resolution biological data

Probabilistic modelling provides a principled framework for model-based inference from data. Probabilistic modelling using Gaussian process inference has been used to gain insights into single-cell and spatial omics data [1,2,3]. Read more
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