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Academic jobs in Großbritannien

UKRI Centre for Doctoral Training in Environmental Intelligence: Data Science & AI for Sustaina[...]

The International Society for Bayesian Analysis

Exeter
Vor Ort
GBP 15.000 - 18.000
Vor 27 Tagen
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Senior Member of Research Staff (London)

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UKRI Centre for Doctoral Training in Environmental Intelligence: Data Science & AI for Sustaina[...]
The International Society for Bayesian Analysis
Exeter
Vor Ort
GBP 15.000 - 18.000
Vollzeit
Vor 27 Tagen

Zusammenfassung

A leading academic institution in Exeter offers 10 fully-funded PhD places in Environmental Intelligence, focusing on data science and AI for sustainable futures. Candidates with a strong academic background in relevant fields are encouraged to apply. The program includes full funding for tuition, a stipend, and research activity support. Applications are competitive and should be submitted via email by specified deadlines.

Leistungen

Fully funded studentship
Annual stipend
Opportunity for research activities

Qualifikationen

  • Expected to receive a relevant undergraduate degree, such as in computer science or statistics.
  • For international students, minimum IELTS 6.5 required.
  • Additional training provided for non-computer science/mathematics backgrounds.

Aufgaben

  • Engage in a 4-year training programme with supervision of PhD research.
  • Work with external partners including the Met Office.
  • Participate in research activities and gain practical experience.

Kenntnisse

Data analysis
Environmental challenges expertise
Stakeholder engagement
Governance and ethics understanding

Ausbildung

1st class or 2i undergraduate degree
Jobbeschreibung
UKRI Centre for Doctoral Training in Environmental Intelligence: Data Science & AI for Sustainable Futures: 10 funded PhD places

Mar 13, 2019

Many of the most important problems we face today are related to the environment. Climate change, healthy oceans, water security, clean air, biodiversity loss, and resilience to extreme events all play a crucial role in determining our health, wealth, safety and future development. The vision of this Centre for Doctoral Training (CDT) is to provide a world‑class training environment in Environmental Intelligence (EI): the integration of data from multiple inter‑related sources to provide the evidence and tools that are required for informed decision‑making, improved risk management, and the technological innovation that will lead us towards a more sustainable interaction with the natural environment.
Students will receive training in the range of skills required to become leaders in EI:
(i) the computational skills required to analyse data from a wide variety of sources;
(ii) expertise in environmental challenges;
(iii) an understanding of the governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders.
The training programme has been designed to be applicable to students with a range of backgrounds. Supervisors cover a range of disciplines and experiences related to the use of data in addressing environmental challenges. Students will have the opportunity to work with the CDT’s external partners, including the Met Office, and a range of international institutions and businesses, to ensure that they are well versed in both cutting edge methodology and on‑the‑ground policy and business implementation.

First cohort: ten fully‑funded places are available to start in September 2019.

We welcome applications for this CDT in Environmental Intelligence (EI) to start in September 2019. Applications are made for entry to the 4‑year training programme, including training in the fundamentals of EI and supervision of your PhD research.
Fully funded 4‑year studentships are available for UK and EU students. The funding is for four years and covers University tuition fees and all course fees, an annual stipend (which is £15,009 for the academic year 2019/20), and funds towards research activities. A limited number of studentships are available for exceptional international applicants. Self‑funded students are welcome to apply.

Entry requirements

We welcome applications from those who are expected to receive a 1st class or 2i undergraduate degree in a wide variety of subjects relevant to the application of Data Science and AI to environmental challenges. These might include, for example, computer science, statistics, mathematics, climate, health, economics, philosophy, and social and environmental sciences. For those without a computer science/mathematics background, additional training will be provided (if required) both before and after joining the CDT. We are happy to discuss alternative requirements for those with non‑standard qualifications and/or experience.
For international students, the minimum requirement for entry to the CDT is IELTS 6.5 (with at least 6.0 in each of the four components), or equivalent.

Applying to the CDT

Initially, expressions of interest should be sent by email: ei@exeter.ac.uk. If you would like to have an informal discussion about the CDT, please contact us and we will arrange a suitable time for you to talk to one of the team.

Expressions of interest should include:

  • Title, First Name, Surname, Email address
  • A short statement (no more than 250 words) explaining your motivation for applying to this CDT
  • A 2‑page CV which includes your academic and work experience, your nationality and country of normal residence (for the past 3 years, not including full time education)
  • Scans of your academic transcript(s)
  • Details of where you heard about this CDT

An initial selection will be made by a recruitment panel and selected candidates will be invited to an interview (that can be conducted by Skype).

Deadlines for submission

Application deadlines for the first cohort will be on 31st March 2019, 31st May 2019 and 31st July 2019. Interviews for those in the first round of applications are expected to start in April 2019. You are advised to apply early as the application process is competitive.

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