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PhD in Statistics and Machine Learning

Posted 4 Feb 2024
Work experience
1 to 3 years
Full-time / part-time
Job function
€2,770 - €3,539 per month
Degree level
Required languages
English (Fluent)
Dutch (Fluent)

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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.

Your profile

  • 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.

Our offer

The preferable start date for this position is May 1st, 2024.

The compensation package is competitive at the European level and includes several fringe benefits. Favourable tax agreements may apply to non-Dutch applicants.

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%).

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.

The University of Amsterdam is one of the largest comprehensive universities in Europe. With some 40,000 students, 6,000 staff, 3,000 PhD candidates, and an annual budget of more than 850 million euros, it is also one of Amsterdam’s biggest employers.
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