The DTC provides an interdisciplinary programme that trains PhD students in neuroinformatics and computational neuroscience. This page describes what our students have done during the four-year programme.
In the first four months, students take the full-time Neuroscience module from the MSc in Neuroscience, along with DTC-specific supplementary material designed to make this module accessible to physical scientists and to ensure coverage of a broad range of topics. This module introduces students to the full range of research problems and experimental methodologies in the study of the nervous system, from molecular to cognitive approaches. Students also take up to 2 Informatics courses, typically Neural Computation (NC) and Probabilistic Modelling and Reasoning (PMR) or Information Theory (IT).
In the following four months (semester 2), students study the core themes in neuroinformatics and the computational and electronics methodologies for addressing current topics within these areas. Students will attend modules in Informatics, chosen as appropriate combinations from a list including: Computational Cognitive Neuroscience (CCN), Topics in Cognitive Modelling (CM), Neural Information Processing (NIP), Computational Neuroscience of Vision (CNV), Bioinformatics 2 (BIO2), Applied Databases (AD), Machine Learning and Sensorimotor Control (MLSC), Introduction to Vision and Robotics (IVR) and Neuroinformatics Research (NR). A full list of options is provided to students before course registration is required.
In the final four months, students will undertake one or two research projects. These research projects will typically be based in experimental laboratories so that students can gain an appreciation of the complexities involved in experimental work, and can learn how to relate experimental data to theory.
By the end of the year, students will have been exposed to a broad range of research problems in the neurosciences and cognitive sciences, and will have studied informatics and engineering approaches to tackling these problems.
PhD year 1
Students start developing their PhD topic and proposal by the end of the MSc year, and begin their projects early in their first PhD year. In addition, during the first four months (term 1), the student can attend further specialised courses that are particularly relevant to their research. The modules will be drawn from the broad range of MSc programmes in the relevant disciplines.
Even once the PhD work has begun, students continue to meet with other DTC students regularly for seminars, journal clubs, retreats, and other shared events, allowing them to be part of a robust and lively community of young researchers in related fields.
PhD years 2 and 3
The final two years of the programme are comparable with most existing UK PhD programmes, except that DTC students will be encouraged to spend a 3-6 month period in an institute outside Edinburgh that has collaborative links with their supervisor’s laboratory. The aim of this period of external study is to familiarise students with research facilities outside the host institution, to give them a different perspective on their research project and to enable them to make contacts with research groups where they may commence their post-doctoral career. The student will normally complete preparation of their PhD thesis by the end of the third year.
Need and Career Prospects
Students graduating from the programme can choose from a variety of career options. The majority of our current students aspire to an academic career (70%), while the others are planning a career in industry (20%) or in science communication (10%). With the recent emergence of many centres combining informatics and life sciences research, the current academic job market is very healthy.
To assess industrial demand for our neuroinformatics graduates, we commissioned a report from Alta Biomedical on future employment options in industry. A total of four UK and 13 global companies in the areas of pharmacy, devices and clinical diagnostics, and information services were interviewed. All companies required systems expertise to handle the ever-increasing amount of data generated. In all sectors, a strong need in the intermediate future for computational modelling was reported.
In general, candidates with multi-disciplinary backgrounds are highly rated by employers, as they have a proven ability to be flexible and adapt to an ever-changing environment. The communication skills that students develop in interacting with different scientists are essential for leadership in modern employment. DTC students are thus highly competitive in the global marketplace for research professionals.