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Position: Post-doctoral Training Fellow - Image analysis
Institution: Institute of Cancer Research
Location: London, United Kingdom
Duties: To develop new image processing tools for analysing cancer histological images through programming in Python, Matlab, C or equivalent language. To use statistical tools for further analysis of data generated from image analysis. To contribute to the publication of high quality research in the form of papers, patents, and presentations at meetings. To work independently on a defined project and as part of a team, and to consult when appropriate. To communicate effectively with other members of the team and collaborators, where necessary, ICR and outside organisations. Be familiar with ICR’s approach towards risk management including its policies and procedures, which require all staff to play an active part in identifying and managing risk
Requirements: PhD in computer science, statistics, engineering or related subjects. Image analysis, machine learning and statistics. Programming in Python, Matlab, C, R or equivalent languages. Good publication record. Good interpersonal skills and the ability to interact effectively with collaborators. Ability to interpret and present results in conferences. Highly self-motivated with scientific curiosity and a keen desire to produce high quality scientific data
Text: Post-doctoral Training Fellow - Image analysis Closing Date 23/09/2018, 23:55 Location Sutton Division Molecular Pathology Team Computational Pathology & Integrated Genomics Vacancy Type Full time Type of Contract Fixed Term Length of Contract 3 years Hours per Week 35 Salary Range £30,715 - £40,409 Vacancy Description We seek a highly motivated postdoc to join the Computational Pathology and Integrative Genomics team led by Dr. Yinyin Yuan. This is part of an exciting initiative between ICR and the Arizona Cancer and Evolution Center led by Prof. Carlo Maley ( ) to study cancer evolution and ecology. ICR were ranked first in the Times Higher Education league table of UK university research quality from the most recent Research Excellence Framework (REF 2014) for our high impact publications. We are world leaders in identifying cancer genes, discovering cancer drugs and developing precision radiotherapy. Together with our hospital partner The Royal Marsden, we are rated in the top four centres for cancer research and treatment worldwide. Our Cancer Research UK Cancer Therapeutics Unit is the largest academic cancer drug discovery and development group worldwide. We discover more new cancer drugs than any other academic centre in the world. The main focus of the Yuan lab is to develop new computer vision and deep learning tools for large-scale analysis of tumour pathological images ( ). We have pioneered the use of spatial statistics to quantify spatial intra-tumour heterogeneity, a fundamental but under-explored biological feature of tumours. The successful candidate will lead the digital pathology component of a large collaborative program that integrates imaging with omics data. The successful post holder will provide machine learning expertise for analysing histological images and developing new programs. He/she will enjoy the highly collaborative environment at ICR and in this research program, and work closely with an international, highly interdisciplinary team. He/she will have the opportunities to learn about the cutting-edge technologies including deep learning, bioinformatics, and single-cell sequencing, and travel to conferences and collaborators, excel in coordinating between programming and explore new research areas. Applicants must hold a PhD in Computer Science, Systems Biology, Engineering, Ecology, Physics or Statistics. Knowledge/experience in medicine or deep learning is desirable but not essential. We consider all applications on merit and have a strong commitment to enhancing the diversity of our staff. Job Description and Person Specification File upload field, to activate press space bar Post-doctoral Training Fellow - Image Analysis - Job Description.pdf Additional Documentation for Candidates
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