Warwick. The successful

 Applications are invited for up to three PhD positions in ``Computational

Bayesian Statistics with applications to decentralised machine learning’’

at the Department of Statistics, University of 

applicants will be part of the OCEAN project, a 10M Euro ERC-funded project

involving around 30 researchers in Warwick, Paris and Berkeley. (Further

details below.) These studentships will be based at the University of

Warwick Statistics Department, which houses one of the largest and most

successful groups in Computational Bayesian Statistics worldwide and

successful applicants will join a vibrant local research community, as well

as having the opportunity to interact closely with other researchers within

the OCEAN consortium.

 

 

 

Applicants should have an interest and some background in Computational

Bayesian methods are interpreted widely, and applicants with more theoretical,

more applied and/or more computational interests are equally encouraged to

apply. Background in machine learning, privacy, game theory, federated

learning and high-performance computing would be helpful for some topics

but are not essential.

 

 

 

Successful applicants will have Ph.D. fees covered from OCEAN as well as

receiving an additional stipend beginning at £20,000 and access to a

Research Training and Support fund of at least £5,000 to cover research

equipment and travel costs. Studentships will be for up to 4 years.

 

 

Informal enquiries before this deadline are strongly encouraged (to Gareth

Roberts, Gareth.o.roberts@warwick.ac.uk, or Adam Johansen,

a.m.johansen@warwick.ac.uk ). For more details on how to apply, please read

on.

 

 

 

 

How to apply?

 

==============

 

 

 

Applicants should apply directly to the Warwick PhD admissions portal, all

general information can be found at

 

 

 

[https://warwick.ac.uk/study/postgraduate/apply/research](https://warwick.ac.uk/study/postgraduate/apply/research)

 

 

 

To be considered for these OCEAN positions, you are advised to apply before

January 8th 2024 (after which we shall interview and make offers). (After

this date, applicants are still welcome to apply but run the risk that

positions will have already been filled.)

 

 

 

On your application it is important that you include the following:

 

 

 

1. You should apply to the Statistics Department.

 

 

 

2. Mention OCEAN explicitly in the personal statement part of the

application. Please also mention it in the source of funding section. If

you are potentially interested in other PhD opportunities in Warwick

Statistics, you can indicate that also.

 

 

 

3. Also include in your personal statement information about your

motivation and suitability for the OCEAN project. Precise information about

an area you wish to work in is not necessary, although an indication about

your research interests would be very helpful.

 

 

 

4. Please ensure that your referees are aware that they will need to upload

their supporting statements by the deadline for these positions as

decisions will be made soon after this deadline.

 

 

 

Competitive applications are likely to require strong Undergraduate and

Masters achievement, typically at first class and distinction levels

respectively for UK applicants, and will most likely have completed degrees

in Statistics, Mathematics, Data Science, Computer Science, although we are

open to consider more unusual routes from motivated applicants.

 

 

 

Informal enquiries before this deadline are strongly encouraged (to Gareth

Roberts, Gareth.o.roberts@warwick.ac.uk or Adam Johansen,

a.m.johansen@warwick.ac.uk ). We'd be very pleased to hear from you and

would be happy to advise about your application during a brief informal

video call if that would be helpful.

 

 

 

 

 

The OCEAN project

 

==================

 

 

 

Until recently, most of the major advances in machine learning and decision

making have focused on a centralised paradigm in which data are aggregated

at a central location to train models and/or decide on actions. This

paradigm faces serious flaws in many real-world cases. In particular,

centralised learning risks exposing user privacy, makes inefficient use of

communication resources, creates data processing bottlenecks, and may lead

to concentration of economic and political power. It thus appears most

timely to develop the theory and practice of a new form of machine learning

that targets heterogeneous, massively decentralised networks, involving

self-interested agents who expect to receive value (or rewards, incentive)

for their participation in data exchanges.

 

 

 

In response to these challenges, OCEAN is an ERC-funded project which aims

to develop statistical and algorithmic foundations for systems involving

multiple incentive-driven learning and decision-making agents, including

uncertainty quantification predominantly with a Bayesian focus. OCEAN will

study the interaction of learning with market constraints (scarcity,

fairness, privacy), connecting adaptive microeconomics and market-aware

machine learning. To achieve these goals, OCEAN will need to develop new

statistical and machine-learning methodologies, together with algorithms

for sampling and optimisation which are both scalable to large problems,

and have provable theoretical guarantees.

 

 

 

The OCEAN project is led by Eric Moulines (Ecole Polytechnique, Paris),

Michael Jordan (Berkeley), Christian Robert (Dauphine, Paris) and Gareth

Roberts (Warwick) and involves a consortium of around 30 researchers.