APPLY


CURRENTLY RECRUITING FOR INDUSTRIAL COLLABORATIVE STUDENTSHIPS. DEADLINE 26th JUNE 2019.


For more information click 'Apply for industrial CASE studentships' below. All our DTP places have been filled, only the projects listed below are still available for applications.



This 4-year programme is aimed at graduates with a strong interest in multi-disciplinary research. We invite applications from highly motivated students from a wide range of academic backgrounds including biological, biomedical, veterinary, physical, computational, engineering or mathematical disciplines.

 



This 4-year programme is aimed at graduates with a strong interest in multi-disciplinary research. We invite applications from highly motivated students from a wide range of academic backgrounds including biological, biomedical, veterinary, physical, computational, engineering or mathematical disciplines.



Please ensure that you read the Guidelines before submitting an application.

Your application and supporting documents should be sent in a single email to LIDo.Admissions@ucl.ac.uk
Your application must be complete, including both references, by the deadline of 11th January. To ensure sufficient time to contact your referees it is highly recommended that you submit your application a minimum of 1 week before this date.

Download the APPLICATION GUIDELINES here.

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Download BOTH sections of the Application Form here:
SECTION A
SECTION B
Or as a single APPLICATION FORM (ZIP FILE)

Students who wish to apply for an iCASE project (please see below), should also return the relevant form indicating their project preference.



Your application and supporting documents should be sent in a single email to LIDo.Admissions@ucl.ac.uk Your application must be complete, including both references, by the deadline of Wednesday 26th June at 5pm. To ensure sufficient time to contact your referees it is highly recommended that you submit your application a minimum of 1 week before this date.

PLEASE ENSURE TO STATE CLEARLY WHICH PROJECT YOU ARE APPLYING FOR

Download the APPLICATION GUIDELINES here.

Download BOTH sections of the Application Form here:
SECTION A
SECTION B
Or as a single APPLICATION FORM (ZIP FILE)

  • Rapid fabrication of designer genome-wide yeast libraries.

    Supervisors:
    Dr. Peter Thorpe, Senior Lecturer in Biochemistry, Queen Mary University of London;
    Dr. Harry Singer, Singer Instruments Ltd.

    Creating a proteome-wide library of tagged proteins has proven immensely useful and has been pioneered in the widely-used model system budding yeast (Saccharomyces cerevisiae). Many researchers, regardless of the biological problem they are addressing, can conceive of a library that would allow them to answer key questions in their field. However, creating entire novel libraries is both time consuming and costly even utilising a powerful model system such as yeast. We propose to create a system (Rapid Tag Switching or RTS) to enable researchers to create custom libraries of yeast strains, each strain encoding a different tagged protein, in as little as one week. Such libraries could encode any genetically-encoded tag conceivable. Examples could include ‘switchable’ fluorophores for high-throughput super-resolution imaging, or conditional degrons, which would facilitate study of essential proteins across the genome – the possibilities are endless. For our own studies on the kinetochore, we wish to define how specific genetic changes impact upon the process of segregating chromosomes during cell division, since mis-segregated chromosomes are a hallmark of cancer cells (see reference list). We will use RTS to create libraries of strains encoding novel fluorophores, both for multi-channel imaging and super-resolution imaging that will allow us to quantitatively measure such changes and map the position of kinetochore regulators. Additionally, we would aim to create a library of strains where each protein can be conditionally degraded or ‘knocked sideways’ (removed to a specific location within the cell). These libraries would be used to create specific alterations that result in chromosome mis-segregation and define how these changes affect cells – essentially modelling the changes seen in cancer cells.

  • Investigating erosion and abrasion on natural enamel

    Supervisors:
    Professor David Bartlett, King's College London
    Saoirse O’Toole, Unilever

    Erosive toothwear is the fourth most common dental condition, affecting up to 30% of European adults. Factors impacting erosive toothwear include acid exposure frequency (e.g. diet or stomach acid reflux) and abrasive factors (e.g. brush, tongue). Prevention of the condition on enamel is key to the control without which the condition can affect the whole dentition and potentially cost between £5000-£30,000 to restore. Little is known about the protective effects of pellicle, a salivary protein layer that naturally forms on the tooth surface, and how this can be modified to enhance protection of enamel from acid and abrasive factors. In addition, the role of enamel remineralisation agents (e.g. fluoride and calcium) on prevention of erosive toothwear processes is not well understood.
    This project follows a successful collaboration between Unilever Oral Care and the toothwear team at Kings. Together we have developed world beating science to measure erosive toothwear on complex topographical natural tooth surfaces. This unique skill creates laboratory models far superior to the conventional polished enamel surfaces. The next step in the development is to investigate how abrasion delivered by a toothbrush and toothpaste interacts with dietary acids on natural enamel, with or without salivary pellicle. The effect of differing times of acid exposure (matched to dietary acid concentration) to varying abrasivity of toothpastes and pressure applied to the brush will test the interaction between acids and abrasion. It is believed that this interaction is the main reason why erosive toothwear progresses, but the dynamics are unknown. In addition, the role of established remineralising agents in protecting and repairing the enamel surface is likely to be a factor in the overall erosion/abrasion process. Therefore, the impact of fluoride and calcium-based technologies on erosion/abrasion will be evaluated.

  • Influenza A virus evolution: Determining the key factors that influence emergence of influenza variants of animal and public health significance

    Supervisors:
    Dr. Nicola Lewis, Royal Veterinary College
    Prof, Ian Brown, Animal & Plant Health Agency

    Influenza viruses circulating in animal hosts continue to cause epidemics with both morbidity and mortality. These viruses constantly evolve genetically and antigenically, so presenting a fundamental challenge when implementing control measures such as vaccination. Not only do these viruses impact on global food security through their devastating effects on animal health, but they also have pandemic potential, particularly when different emerging variants occur through reassortment.
    Here the student will employ integrated multi-disciplinary approaches to characterize influenza viruses circulating in birds and pigs. These analyses will span the antigenic and genetic properties of emerging strains, their relationship to current vaccine strains and explore the ever-evolving evolution of these key pathogens. The risk of emergence will be modelled, incorporating both immunological and virological aspects at the individual and population levels.

    Professor Ian Brown will be oversee the studentship at APHA. The student will have access to the facilities at the OIE/FAO International Reference Laboratory for avian influenza, swine influenza and Newcastle Disease. Dr Nicola Lewis will offer guidance and expertise on state-of the art computational analyses for influenza viruses of risk to animal and human health, based at the RVC. The studentship will be split approximately 60:40 between laboratory and computational science.

  • Mapping human brain development at new spatial resolutions using machine learning and 7T MRI

    Supervisors:
    Dr David Carmichael, King's College London
    Prof. Daniel Alexander, University College London

    In this PhD project we aim to bring together cutting edge advances in brain imaging and computer vision (that are also termed machine learning or artificial intelligence). MRI is the most important imaging modality for studying human brain development owing to its non-invasive nature and its flexibility to quantify different aspects of brain structure and function. Firstly, we want to use the sensitivity of new 7 Tesla MRI scanners to enable brain imaging at higher resolution. This is important because to image cortical development, the layered cortex (1.5-4mm thick) needs sub-millimetre sampling yet typical resolutions currently achieved (~1mm) are insufficient.
    Rapid developments in computer vision based on machine learning can enable computers to learn the mapping between high resolution and low resolution data, with pioneering work already performed in this area by the supervisory team. Once this mapping is understood it will allow us to enhance the resolution of existing low resolution MRI scans, and further, use this enhanced data to improve our models of brain development.

  • Identification of genomic biomarkers associated with hypertrophic cardiomyopathy (HCM) in cats

    The deadline for applications to this project only is 7th July

    Supervisors:
    Dr Androniki Psifidi, Royal Veterinary College
    Professor Virginia Luis Fuentes, Royal Veterinary College
    Dr Rudiger Raue, Zoetis

    Hypertrophic cardiomyopathy (HCM), the most common cardiac disease in humans and cats, occurs spontaneously in 15% of domestic cats and may cause heart failure, cardioembolic complication or death. Sudden onset life-threatening clinical signs cause distress to cat owners and frustration to veterinary surgeons. Despite the severity and prevalence of HCM, treatment solutions are currently limited. Genomic studies could provide insights into the underlying molecular mechanisms resulting in HCM and lead to the development of targeted therapeutics and diagnostics, as in humans.
    There is evidence that the disease is heritable, and a genetic association with an HCM phenotype has already been identified in the Maine coon and Ragdoll breeds. Previous studies in domestic cats used a candidate gene approach based on knowledge from human studies. These studies identified two HCM-associated mutations in the myosin binding protein C3 (MYBPC3) gene. However, the candidate gene approach has failed to identify any further causative mutations.

    This project is aimed at investigating the genetic architecture and the underlying molecular mechanism of HCM susceptibility in cats. Our hypothesis is that HCM is a complex disease of polygenic inheritance controlled by several variants in both protein-coding genes and regulatory elements. Therefore, in the proposed study we will use genome-wide approaches to detect loci and genomic regions affecting HCM occurrence using meticulously phenotyped cats from 2 pedigree breeds.
    With access to myocardial tissues and DNA from cats with HCM and controls, the student will perform genome-wide association studies, next generation sequencing and transcriptomic profiling and assess the results in conjunction with pre-existing and collated extensive phenotypic and epidemiological data.

    The student will be trained in cutting-edge genetic and bioinformatic technologies, and relevant wet lab techniques to explore DNA-array, exome- and RNA- sequencing data to identify genetic markers, causative genes and gene networks underlying feline HCM. This project is in collaboration with Zoetis, one of the world leading companies in animal health. The student will interact with experts in drug discovery while he/she will undertake a placement in the R&D site in Belgium working within a team where the focus will be on preclinical pharmaceutical development based on the findings of the project. There will also be opportunity to collaborate directly with leading international veterinary and human research teams working on HCM projects during the course of the PhD.



Applicants must hold, or be expected to achieve, a first or high upper second-class undergraduate honours degree or equivalent (for example BA, BSc, MSci) or a Masters degree in a relevant subject.

Applicants must be confident using computers and show some evidence of numeracy (minimum GCSE mathematics or statistics or an equivalent or alternatively a university degree module with a good grade).

All students whose first language is not English must be able to provide recent evidence that their spoken and written command of the English language is adequate for the programme. The required evidence may be one of the following:

  • Substantial education or work experience conducted in English
  • A recently obtained acceptable English language qualification or test result.Our preferred English language qualification is the International English Language Testing System (IELTS) Academic Version and we require candidates to achieve the level of "GOOD".
  • Good level: Overall grade of 7.0 with a minimum of 6.5 in each of the subtests.

Candidates who are unsure if they meet the entry criteria should contact us before submitting an application.



Fully funded places include home (UK/EU) tuition fees and a tax-free stipend in the region of £16,777.

EU applicants must meet the UKRI Residency Criteria to be eligible for full funding. Candidates should refer to the The Education (Fees and Awards) (England) Regulations 2007 for more details

Applicants who are applying for DTP places but who are not eligible for fully funded BBSRC studentships may be eligible for fully funded (Home/EU Fees only) Institutional Studentships.

More details are available by contacting the programme administration.