PhD Student Causal Machine Learning

24 May 2024
Job Information

Organisation/Company

Ghent University

Research Field

Computer science » Programming
Computer science » Other
Mathematics » Applied mathematics
Mathematics » Statistics

Researcher Profile

First Stage Researcher (R1)

Country

Belgium

Application Deadline

1 Sep 2024 - 21:59 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

38

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Last application date Sep 01, 2024 00:00

Department WE02 - Department of Applied Mathematics, Computer Science and Statistics

Contract Limited duration

Degree Master of Statistics (with a strong component on mathematical statistics), Master of Mathematics, or equivalent.

Occupancy rate 100%

Vacancy type Research staff

Job description

The Causal Inference research lab at Ghent University is seeking a highly motivated and talented PhD student to join its team. Ghent University has a long tradition of research in causal inference since the mid 90's, and now has a vibrant causal inference community comprising over 20 statisticians dedicated to advancing this field.

This PhD position is one of multiple positions being opened in connection to Advanced ERC Grant ACME ‘Assumption-lean (Causal) Modeling and Estimation’. In an era where the focus on causal inference is increasingly turning away from modeling towards quantifying population-level intervention effects, there is a risk of oversimplifying causal queries and of neglecting the rich history and efficacy of statistical modeling techniques. This ERC project aims to bridge this gap by leveraging the flexibility and power of statistical models to accurately represent intervention effects or facets of the causal data-generating mechanism, integrating it with recent insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, this project seeks to enhance the robustness and efficiency of debiased machine learning methods. This PhD project will primarily focus on this latter component, in interaction with fellow researchers on this project.

Job profile

We are looking for a highly creative and motivated PhD student with the following qualifications and skills:

• You have (or will obtain before the starting date, i.e., a few months after application) a master's degree in Statistics (with a strong component on mathematical statistics), Mathematics, or equivalent, with excellent (‘honors’-level) grades. Your degree must be equivalent to 5 years of studies (bachelor + master) in the European Union.
• You have a strong background in mathematical statistics; familiarity with empirical process theory, causal inference or debiased machine learning is a plus.
• You are creative and have strong analytical skills.
• You have experience in at least one modern programming language for data analysis (Python, R, etc.)
• You are a team player and have strong communication skills.
• Your English is fluent (C1 CEFR level) both speaking and writing.

Your main tasks will include:

• Studying, implementing, developing and improving state-of-the-art techniques for debiased machine learning, to enhance their robustness and statistical efficiency.
• Using techniques from asymptotic statistics and empirical process theory, along with Monte Carlo simulation studies, to examine the large and finite-sample properties of the developed debiased machine learning estimators.
• Applying the developed techniques for debiased machine learning in substantive case studies with real world medical data.
• Collaborating with fellow researchers to develop foundations for a generic paradigm for assumption-lean causal/statistical modeling.
• Writing high quality publications, targeting top journals and international conferences.
• In addition to your primary research responsibilities, you will actively contribute to the educational mission of our institution by providing (limited) support for courses in (mathematical) statistics. In addition, you can take on a mentoring role by supervising bachelor or master theses related to the subject of this PhD.

WHAT WE CAN OFFER YOU

• We offer a full-time position as a doctoral fellow, consisting of an initial period of 12 months, which - after a positive evaluation, will be extended to a total maximum of 48 months.
• The fellowship amount is 100% of the net salary of an AAP member in equal family circumstances. The individual fellowship amount is determined by the Department of Personnel and Organization based on family status and seniority. A grant that meets the conditions and criteria of the regulations for doctoral fellowships is considered free of personal income tax. Click here for more information about our salary scales
• All Ghent University staff members enjoy a number of benefits, such as a wide range of training and education opportunities, 36 days of holiday leave (on an annual basis for a full-time job) supplemented by annual fixed bridge days, bicycle allowance and eco vouchers. Click here for a complete overview of all the staff benefits  (in Dutch).

How to apply

The PhD position will begin at the earliest beginning of October 2024. Send your CV, transcripts of study results, a motivation letter of maximum 1 page (highlighting why you believe you are a suitable candidate for the position, why you want this position, and what relevant skills you have developed), and at least two reference contacts to [email protected] , with the subject ‘Application: PhD MathStat ACME’. The transcripts of study results at the time of application are not necessarily official (yet), but these will be required upon recruitment.

Please note that starting July 1st, there will be continuous evaluation of the candidates until the vacancy is filled. The application may therefore be closed before the deadline stated here.

For more information about this vacancy, please contact Prof. Stijn Vansteelandt ([email protected] ).

Requirements

Research Field

Computer science

Years of Research Experience

1 - 4

Research Field

Computer science

Years of Research Experience

1 - 4

Research Field

Mathematics

Years of Research Experience

1 - 4

Research Field

Mathematics

Years of Research Experience

1 - 4

Additional Information

Website for additional job details

https://academicpositions.com

Work Location(s)

Number of offers available

1

Company/Institute

Ghent University

Country

Belgium

City

Ghent

Postal Code

9000

Street

Sint-Pietersnieuwstraat 33

Where to apply

Website

https://academicpositions.com/ad/ghent-university/2024/phd-student-causal-machi…

Contact

City

Ghent

Website

http://www.ugent.be/en

Postal Code

9000

STATUS: EXPIRED