PhD in Statistics and Machine Learning

4 Mar 2024
Job Information

Organisation/Company

University of Amsterdam (UvA)

Research Field

Economics

Researcher Profile

First Stage Researcher (R1)

Country

Netherlands

Application Deadline

1 Apr 2024 - 21:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

38.0

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

Are you eager to apply cutting-edge machine learning techniques, develop innovative algorithms, and tackle real-life challenges associated with diagnosing of Alzheimer’s disease? The Business Analytics Section at the Amsterdam Business School (University of Amsterdam ) invites applications for a PhD position in Statistics and Machine Learning. We are looking for highly motivated PhD candidates who aspire to excel in the international academic arena at the highest level.

What are you going to do?
This PhD research initiative aims to develop advanced statistical and machine learning methods to facilitate the early diagnosis of Alzheimer’s disease, a condition that disrupts neural network functionality. Graph-based machine learning techniques are essential for this purpose due to their ability to incorporate network structures. Graph neural networks (GNNs), a subset of deep learning that leverages graph structures, have shown promising results. However, they fall short in quantifying model uncertainty, an essential factor for diagnosing Alzheimer’s disease. Bayesian methods provide mathematically grounded frameworks to address model uncertainty, but often with significant computational demands. The main objective of the research line is to develop a GNN that incorporates Bayesian graphical methods for Alzheimer’s detection. The entire project is divided into two PhD subprojects. The first subproject, currently in progress by an existing PhD student, aims to develop GNNs that are both computationally efficient and grounded in Bayesian principles.

This vacancy is for the second PhD subproject, which intends to apply the Bayesian framework alongside GNNs to analyze real-world data related to Alzheimer's cases. The PhD student will use the Bayesian graphical method to identify the brain structure of Alzheimer's patients and patients with a healthy brain. Subsequently, the PhD student will implement a GNN to categorize brain imagery into either typical brain function or Alzheimer’s affected states, using various imaging modalities like MRI, fMRI, and PET, along with non-imaging data such as demographic and genetic information. Our partnerships with hospitals provide us with access to pertinent data for this research.

Tasks and responsibilities
The PhD student will work in close collaboration with the supervisory team, alongside the current PhD student on this project, and additional academic staff. The responsibilities will encompass:

• Developing and applying advanced statistical and machine learning techniques, in particular, Bayesian statistical methods and graph neural networks;
• developing open-access software tools (such as R and Python packages or C++ libraries) for applying the newly developed algorithms/models and techniques to real-world datasets;
• working in close collaboration with the hospital to understand the data and work on data collection and cleaning;
• writing up findings for publication in prestigious machine learning and statistical journals;
• presenting research findings at leading conferences;
• attending classes and seminars (including those offered at other universities) to further develop thinking and research skills;
• conducting teaching (to a limited degree), including undergraduate tutorials and the supervision of BSc dissertation projects.

Requirements
Specific Requirements

• Master’s degree (preferably a Research Master´s or MPhil degree) in Statistics, Machine Learning, Mathematics, Econometrics, or a related field;
• a strong background in Modern Statistics/Machine Learning/Artificial Intelligence, including Bayesian inference and deep neural networks;
• excellent programming skills in one or more of the following languages: R, Python, C, C++, and a strong willingness to develop these skills further;
• excellent communication, presentation and writing skills;
• an excellent command of English, ideally with experience writing for a scientific audience.

Additional Information
Benefits

The preferable start date for this position is 1 June 2024.

The compensation package is competitive at the European level and includes several fringe benefits. Favourable tax agreements may apply to non-Dutch applicants. To know more about working at the University of Amsterdam, please check this link and uva.nl/working-at-eb .

We offer full-time employment for three or four years (depending on prior education) with an initial period of 18 months, an intermediate evaluation after 18 months and a possibility to extend it for 30 months (four years in total). The end result should be a PhD thesis. An educational plan will be drafted that includes attendance of courses and conferences at home and abroad.

The gross monthly salary will range between €2,770,- in the first year to €3,539,- in the last year for full-time employment (38 hours per week), excluding holiday allowance (8%) and year-end bonus (8.3%).

The Collective Labour Agreement for Dutch Universities is applicable.

In addition, the UvA offers excellent study and development opportunities and encourages employees to continue to professionalise.

What else do we offer you?
The UvA has an extensive package of fringe benefits, including:

• 29 days' holiday with full employment & extra holidays between Christmas and the new year;
• excellent work facilities, including teleworking;
• reimbursement of commuting expenses;
• pension accrual with ABP;
• excellent opportunities for ongoing study and professional development that are strongly supported by the university;
• opportunities to participate in open UvA lectures, earning up to 30 credits per year.

Selection process

Studies show that women and members of underrepresented groups only apply for jobs if they meet 100% of the qualifications. Do you meet the educational requirements but do not yet have all the required experience? Then the UvA encourages you to apply anyway.

Do you recognise yourself in the job profile? Then we look forward to receiving your application before 1 April 2024. The candidate will preferably start by 1 June 2024. You may apply online by using the link below.

Applications in pdf should include:

• A curriculum vitae;
• a letter of motivation including a description of the candidate’s primary research interests and ambition;
• transcripts of secondary school, Bachelor’s and Master’s studies;
• if available, GMAT or GRE scores, preferably recent;
• if available, proof of English proficiency through a TOEFL or IELTS test score, preferably recent.

Shortlisted candidates will be invited for a job interview and may be asked to present their master’s thesis and/or other research work. Moreover, the candidates may be asked to provide two reference letters.

Additional comments

Do you have any questions, or do you require additional information? Please contact: Reza Mohammadi ([email protected] ), Associate Professor

Website for additional job details

https://www.academictransfer.com/338519/

Work Location(s)

Number of offers available

1

Company/Institute

Faculty of Economics and Business

Country

Netherlands

City

Amsterdam

Postal Code

1018WB

Street

Roetersstraat 11

Geofield

Where to apply

Website

https://www.academictransfer.com/en/338519/phd-in-statistics-and-machine-learni…

Contact

City

Amsterdam

Website

http://www.uva.nl/

Street

Spui 21

Postal Code

1012 WX

STATUS: EXPIRED