Translational Neuroscience Research Associate (Stem Cells)

1000scholars

Cambridge

Presencial

GBP 33 000 - 46 000

Tempo integral

14 dias+
Gerador de candidaturas

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Vantagens oferecidas por esta oferta de emprego

Mentorship
Career development
Collaborative environment

Resumo da oferta

1000scholars Cambridge invites applications for a Grade 7 Research Associate/Assistant to lead a project at CCMR, combining human stem cell biology, disease modelling, functional genomics and translational neuroscience.

The successful candidate will develop patient stem cell–derived models of progressive MS, apply CRISPR-Cas9 screens, and push discoveries toward therapies. A PhD is required; strong data analysis and collaboration are essential.

Qualificações

  • PhD in cell biology, biochemistry, molecular biology or related field.
  • Experience culturing human stem cells (iPSCs) and cellular engineering.
  • Strong data analysis and communication skills; track record of publication.
  • Experience with single-cell omics, bioinformatics, epigenomics or cell metabolism desirable.

Responsabilidades

  • Lead a project at the intersection of stem cell biology and translational neuroscience.
  • Develop patient stem cell-derived models of progressive MS.
  • Apply functional genomics approaches to identify disease-modifying targets.

Conhecimentos

Cell biology
Biochemistry
Molecular biology
CRISPR-Cas9
Gene editing
Single-cell omics
Data analysis
Communication

Formação académica

PhD in cell biology, biochemistry, molecular biology, or related field

Ferramentas

Single-cell sequencing
Bioinformatics tools

Descrição da oferta de emprego

1000scholars Cambridge invites applications for a Grade 7 Research Associate/Assistant to lead a project at CCMR, combining human stem cell biology, disease modelling, functional genomics and translational neuroscience.

The successful candidate will develop patient stem cell–derived models of progressive MS, apply CRISPR-Cas9 screens, and push discoveries toward therapies. A PhD is required; strong data analysis and collaboration are essential.

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