Mrc Investigator Scientist In Bioinformatics And Genetics Lms 2509

View Vacancy -- MRC Investigator Scientist in Bioinformatics and Genetics LMS 2509 Open Date 28/10/2024, 10:00 Close Date 20/11/2024, 23:55 Research Institute MRC Laboratory of Medical Sciences Research Institute / Unit Information The LMS is a world-class research laboratory where scientists and clinicians collaborate to advance the understanding of biology and its application to medicine.
Funded by the MRC as part of UK Research and Innovation , the LMS has a collaborative working culture and new state-of-the-art building based in the heart of West London in the Hammersmith Hospital Campus (W12).
The LMS aims to deliver transdisciplinary team science, in synergistic interaction with Imperial College and the wider national research environment.
Part of achieving this, is to recruit new talent and support postdoctoral scientists to become world leaders in their area of science.
UK Research and Innovation is a new entity that brings together nine partners to create an independent organisation with a strong voice for research and innovation, more information can be found at www.ukri.org Band MRC - 4 Location London Salary £41,843 - £43,962 plus London Allowances (£3,913 & £1,472) per annum Contract Type Fixed Term Job Type Science Full Time / Part Time Full Time Contract Length 3 years Job Description Overall purpose: We are seeking a talented investigator scientist to work on a new collaboration on human aging with Calico Labs.
Aging is a key risk factor for cardiovascular disease and recent work has identified several potential mechanisms by which human aging could be attenuated.
This is part of a 3-year program using cardiovascular MRI to assess modifiable mechanisms of aging.
Applicants should have an established track record of research in machine learning, statistics and bioinformatics and will lead on developing models for biological age prediction from multiple image derived phenotypes.
Knowledge of a wide range of statistical tools, clustering / prediction algorithms, as well as causal inference techniques is required.
Applicants will have significant experience in writing code for robust and reproducible analysis and demonstrable experience in bioinformatics pertaining to the analysis of high-throughput data.
Specific experience of using UK biobank data and human imaging would be an advantage.
The applicant should have proven programming experience including Python and R The Computational Cardiac Imaging Group works at the intersection of clinical imaging, bioinformatics, computer vision and molecular cardiology to explore the mechanisms underlying heart function.
The group uses machine learning to analyse cardiac motion for predicting patient outcomes, discovering potential therapeutic targets and identifying genetic risk factors.
We use a flexible and inter-disciplinary approach to research that moves between individuals, populations and model organisms.
The post offers an exciting opportunity to work at the cutting-edge of translational medicine research in a vibrant and supportive multi-disciplinary team that crosses traditional scientific domains.
The role involves performing semi-independent research as well as supporting other members of the group.
Other responsibilities include leading on lab data management duties and liaising with collaborators on project management.
The post holder will have the opportunity to attend scientific conference, and publish the results of their work in leading journals.
There will also be an opportunity to contribute to external grant applications.
There is a vibrant and supportive scientific community at the LMS with outstanding opportunities for career development and training.
Key skills: Background in biomedical data science and statistics Ability to analyse complex genomic data, using a wide range of biological resources and bioinformatic methods including GWAS and rare variant analyses.
Excellent programming skills in R and Python - including use of Jupyter Notebooks and cloud computing resources Advanced knowledge in a range of predictive analytics, clustering techniques and causal inference modelling Knowledge of HPC environments, Git and Docker an advantage Working knowledge of bioinformatics packages also an advantage Main responsibilities: To lead on developing predictive modelling techniques for aging using image derived phenotypes To establish and maintain Jupyter Notebook analyses on DNAnexus Platform To take a management role for large datasets including imaging and genomic resources To lead on curation and version control of analysis software using Git and Docker To project manage the work in collaboration with other members of the team and external partners To write and publish the work, as well as prepare grant submissions To take an active role in the academic work of the group and support the organisation as a whole To present at seminars and conferences as necessary To co-supervise MSc and PhD students Willingness to work out of normal working hours (including weekends) if the requirements of the project demand To contribute to the smooth running of the Group's/Unit's laboratories and, facilities with other scientists, clinicians, technicians and students within the laboratories To train and supervise undergraduate and postgraduate research students, research assistants and postdoctoral scientists as appropriate To comply with the Institute, College, Division, and Unit safety practices and to attend courses on safety when appropriate Any other duties as may be deemed reasonable by Head of group as well as Head of Division Person Specification Education / Qualifications / Training required (will be assessed from application form): Essential: PhD (or equivalent experience) in Data Science / Statistics or a closely related discipline Significant postdoctoral research experience in bioinformatics Knowledge and experience (will be assessed from application form and at interview): Essential: Excellent skills in R and Python with proven ability in programming and handling large datasets Previous experience of executing Jupyter Notebooks in local or cloud environments Previous experience in biomedical data science including clustering and prediction algorithms with a significant track record of relevant publications and presentations Experience of supporting and supervising students Excellent written communication skills in English Desirable: Previous experience of DNAnexus or UKB Research Analysis Platform Ability to perform GWAS and rare variant analyses on WES in patient populations.
Experience in writing Bash scripts and using Linux environments Familiar with HPC scheduling and using Docker Good knowledge of Git for version control Experience of using bioinformatics packages and pipeline development Experience of managing large databases and data wrangling Track record of software development and package creation Experience of lab management Personal skills / Behaviours / Qualities (will be assessed at the interview): Essential: Ability to conduct reproducible research Ability to conduct a detailed review of recent literature Ability to develop and apply new concepts Creative approach to problem-solving Excellent verbal communication skills in English and the ability to deal with a wide range of people Ability to direct the work of a small research team and motivate others to produce a high standard of work Ability to organise own work with minimal supervision Ability to prioritise own work in response to deadlines Advanced computer skills, including word-processing, spreadsheets and the Internet Willingness to work as part of a team and to be open-minded and cooperative Flexible attitude towards work Discipline and regard for confidentiality and security at all times Willingness to work out of normal working hours (including weekends) if the requirements of the project demand Willingness to undertake any necessary training for the role Further Information Applicants should submit a CV, a brief cover letter describing scientific interests and names and contacts of two scientific references.
Please quote reference number LMS 2509.
The MRC is a unique working environment where scientific researchers and professional support staff can actively partake in world class innovation and collaboration opportunities and their skills and knowledge through accessing a wide catalogue of training & development, including professional registration with the Science Council.
In addition, MRC (part of UKRI) provides its community of employees access to a whole host of useful benefits, including a defined benefit pension scheme and excellent holiday entitlement (30 days plus 2.5 privilege days & 8 bank holidays), family friendly policies (6 months full pay maternity & adoption leave), a range of shopping/travel discounts, access to our Employee Assistant Programme Scheme, Health and Wellbeing Support and a salary sacrifice cycle to work scheme.
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