Cross-disciplinary post-doctoral fellow training scheme in quantative biomedicine (XDF) - 8 x 4 year post-docs
Lead Research Organisation:
University of Edinburgh
Abstract
2018 Intake:
Dr. Andrew Papanastasiou - final project title: Uncovering the role and regulation of transcriptional variability in the developing eye
Dr Chantriolnt-Andreas Kapourani - final project title: Spatial modelling of transcriptomic changes in tissue pathology
Dr Mattia Marenda - final project title: Understanding how nuclear meshes of RNA and proteins regulate the structure of transcriptionally related protein complexes
Dr Ava Khamseh - final project title: Recurrent clonal expansion trajectories and mutational competition in a model of very early oncogenesis
2019 Intake:
Dr Vanessa Smer Barreto - final project title: Discovery of new senolytics for anticancer therapy via machine learning
Dr Lyndsay Kerr - final project title: Understanding the Dynamics of DNA Methylation in Cancer Using Mathematical Models
Dr Eric Latorre Crespo - final project title: Time evolution of age-related clonal hematopoiesis – Mathematical modelling and deep learning approaches to uncover disruptive genetic and epigenetic patterns
2020 Intake:
Dr Didier Devaurs - final project title: TBC
Dr. Andrew Papanastasiou - final project title: Uncovering the role and regulation of transcriptional variability in the developing eye
Dr Chantriolnt-Andreas Kapourani - final project title: Spatial modelling of transcriptomic changes in tissue pathology
Dr Mattia Marenda - final project title: Understanding how nuclear meshes of RNA and proteins regulate the structure of transcriptionally related protein complexes
Dr Ava Khamseh - final project title: Recurrent clonal expansion trajectories and mutational competition in a model of very early oncogenesis
2019 Intake:
Dr Vanessa Smer Barreto - final project title: Discovery of new senolytics for anticancer therapy via machine learning
Dr Lyndsay Kerr - final project title: Understanding the Dynamics of DNA Methylation in Cancer Using Mathematical Models
Dr Eric Latorre Crespo - final project title: Time evolution of age-related clonal hematopoiesis – Mathematical modelling and deep learning approaches to uncover disruptive genetic and epigenetic patterns
2020 Intake:
Dr Didier Devaurs - final project title: TBC
Technical Summary
A post-doctoral level Programme for physicists, chemists, mathematicians, statisticians, engineers, computer scientists etc. seeking training to become leaders in Quantitative Biomedicine.
Cross Disciplinary Fellowships
Background
The beginning of the 21st century has seen enormous advances in science and technology. With the completion of the Human Genome Project and implementation of multiple “Big Data” approaches in biomedical sciences, there is now a pressing need to train a new generation of mathematically-minded biomedical scientists who will be able to bridge the gap between life sciences and mathematics/physics/informatics, and efficiently link modern biomedical research with big data research technologies. To address this need a pioneering Cross-Disciplinary Post-Doctoral Fellowships programme (XDF) has been initiated at the University of Edinburgh with matching financial support from the Medical Research Council.
The University of Edinburgh is one of the world leading research universities (ranked 4th in UK for its research power) and is at the forefront of both computational sciences and health sciences. Informatics is the largest and strongest computer science department in the UK (1st for research power according to REF2014), with particular strengths in data science and computational biology. Clinical medicine has been ranked 4th in the UK (research power) with the MRC Institute of Genetics and Molecular Medicine (IGMM) being one of the biggest biomedical research establishments in the country. The XDF Programme lead, Professor Ponting, was trained first in particle physics before pursuing a successful career in biomedicine, so knows first-hand the skills necessary for Fellows to transition into “Big Data Biomedicine”. The programme is led by a Board of Directors, including investigators from the Institute of Genetics & Molecular Medicine and the School of Informatics, who provide Fellows with diverse perspectives.
Purpose
The fellowships are aimed at early-career quantitatively trained scientists, whose ambition is to achieve an independent career in data-driven computational biomedicine. Fellows follow a personalised training and research programme to become truly cross-disciplinary leaders in quantitative biomedicine. Fellows are expected to gain analytical and computational expertise, and an in-depth appreciation of biomedical and health research. They are motivated to address biomedical questions, to apply and train others in their previously acquired analytical/computational skills, and to learn the strengths and limitations of biomedical science methods. Fellows propose a well-developed, important and innovative biomedical project only after substantial relevant training.
Fellowships are funded jointly by the MRC and the University of Edinburgh (with subsequent incorporation of other funds). Fellows receive mentorship from both computational and biomedical scientists, and can use office space in both Informatics and IGMM. Where appropriate, the research may also be conducted in collaboration with an industrial partner and/or the NHS. After their initial year, fellows focus on original research and produce material for peer-reviewed publications and for dissemination at national and international level.
Cross Disciplinary Fellowships
Background
The beginning of the 21st century has seen enormous advances in science and technology. With the completion of the Human Genome Project and implementation of multiple “Big Data” approaches in biomedical sciences, there is now a pressing need to train a new generation of mathematically-minded biomedical scientists who will be able to bridge the gap between life sciences and mathematics/physics/informatics, and efficiently link modern biomedical research with big data research technologies. To address this need a pioneering Cross-Disciplinary Post-Doctoral Fellowships programme (XDF) has been initiated at the University of Edinburgh with matching financial support from the Medical Research Council.
The University of Edinburgh is one of the world leading research universities (ranked 4th in UK for its research power) and is at the forefront of both computational sciences and health sciences. Informatics is the largest and strongest computer science department in the UK (1st for research power according to REF2014), with particular strengths in data science and computational biology. Clinical medicine has been ranked 4th in the UK (research power) with the MRC Institute of Genetics and Molecular Medicine (IGMM) being one of the biggest biomedical research establishments in the country. The XDF Programme lead, Professor Ponting, was trained first in particle physics before pursuing a successful career in biomedicine, so knows first-hand the skills necessary for Fellows to transition into “Big Data Biomedicine”. The programme is led by a Board of Directors, including investigators from the Institute of Genetics & Molecular Medicine and the School of Informatics, who provide Fellows with diverse perspectives.
Purpose
The fellowships are aimed at early-career quantitatively trained scientists, whose ambition is to achieve an independent career in data-driven computational biomedicine. Fellows follow a personalised training and research programme to become truly cross-disciplinary leaders in quantitative biomedicine. Fellows are expected to gain analytical and computational expertise, and an in-depth appreciation of biomedical and health research. They are motivated to address biomedical questions, to apply and train others in their previously acquired analytical/computational skills, and to learn the strengths and limitations of biomedical science methods. Fellows propose a well-developed, important and innovative biomedical project only after substantial relevant training.
Fellowships are funded jointly by the MRC and the University of Edinburgh (with subsequent incorporation of other funds). Fellows receive mentorship from both computational and biomedical scientists, and can use office space in both Informatics and IGMM. Where appropriate, the research may also be conducted in collaboration with an industrial partner and/or the NHS. After their initial year, fellows focus on original research and produce material for peer-reviewed publications and for dissemination at national and international level.
Publications

Aitken SJ
(2020)
Pervasive lesion segregation shapes cancer genome evolution.
in Nature

Argelaguet R
(2019)
Multi-omics profiling of mouse gastrulation at single-cell resolution.
in Nature

Beentjes SV
(2020)
Higher-order interactions in statistical physics and machine learning: A model-independent solution to the inverse problem at equilibrium.
in Physical review. E

Behring A
(2019)
Higher Order Corrections to Spin Correlations in Top Quark Pair Production at the LHC.
in Physical review letters

Cossu G
(2019)
Machine learning determination of dynamical parameters: The Ising model case
in Physical Review B

Devaurs D
(2022)
Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data.
in Journal of the American Society for Mass Spectrometry

Fernández-Duran I
(2021)
Cytoplasmic innate immune sensing by the caspase-4 non-canonical inflammasome promotes cellular senescence.
in Cell death and differentiation


Hall-Swan S
(2021)
DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.
in Computers in biology and medicine

Kapourani CA
(2019)
Melissa: Bayesian clustering and imputation of single-cell methylomes.
in Genome biology