PhD student in Machine Learning

Are you willing to become a researcher in machine learning, and do you want to contribute to shaping the future of data science techniques and applications with an eye on society? Then Vrije Universiteit Amsterdam would like to get to know you.

Location: AMSTERDAM
FTE: 0.8 - 1

We are looking for a new PhD student who will do research in the area of Machine Learning (ML) and in particular on supervised learning and Reinforcement Learning (RL). The position is embedded in the Quantitative Data Analytics group. The group focuses on both fundamental and application-driven research in ML. We are looking for candidates with experience in the area of machine learning, with a special preference for those having expertise in one or more of the following areas: reinforcement learning, explainability of ML methods, the utilization of domain knowledge in ML, and data efficient ML methods. We are interested in attracting a PhD student able to perform ground-breaking research in fundamental aspects of Machine Learning and Reinforcement Learning. On top the PhD student should be able to apply these novel algorithms in the context of an EU funded project called ICARE4OLD focused on improving care for elderly by machine learning based recommendations on interventions. Within the project, a wealth of historical data on elderly care is available. The goal of the PhD thesis will be to develop learning algorithms that can contribute to better predictions on the most suitable interventions in the health context studied in the ICARE4OLD project, meaning that these algorithms should: (1) be explainable; (2) learn from sequential data, and (3) cope with highly variable and complex data originating from multiple countries with different care systems.

Your duties
The PhD position is focused on performing research towards obtaining a PhD degree and contains the following duties:

• Performing research in the field of machine learning and publish in top ML conferences;
• Contributing to some teaching activities and possibly supervising Bachelor and Master students (20% of the time).

We will base our selection on the following components:

• A master degree in an area relevant to machine learning;
• A strong record of computer programming, with experience in pytorch and/or tensorflow;
• Prior experience in machine learning and ideally also on applications in the health domain, demonstrated by relevant courses and master thesis. Having publications in the area is highly valued as well.
• Demonstrated ability to work both independently and as a team member.

A challenging position in a socially involved organization. The salary will be in accordance with university regulations for academic personnel and amounts €2,434 (PhD) per month during the first year and increases to €3,111 (PhD) per month during the fourth year, based on a full-time employment. The job profile: is based on the university job ranking system and is vacant for at least 0.8 FTE.

The appointment will initially be for 1 year. After a satisfactory evaluation of the initial appointment, the contract will be extended for a total duration of 4 years.
Additionally, Vrije Universiteit Amsterdam offers excellent fringe benefits and various schemes and regulations to promote a good work/life balance, such as:

• a maximum of 41 days of annual leave based on full-time employment,
• 8% holiday allowance and 8.3% end-of-year bonus,
• a wide range of sports facilities which staff may use at a modest charge,
• contribution to commuting expenses,
• discounts on collective insurances (healthcare- and car insurance);

The ambition of Vrije Universiteit Amsterdam is clear: to contribute to a better world through outstanding education and ground-breaking research. We strive to be a university where personal development and commitment to society play a leading role. A university where people from different disciplines and backgrounds collaborate to achieve innovations and to generate new knowledge. Our teaching and research encompass the entire spectrum of academic endeavor – from the humanities, the social sciences and the natural sciences through to the life sciences and the medical sciences.

Vrije Universiteit Amsterdam is home to more than 26,000 students. We employ over 4,600 individuals. The VU campus is easily accessible and located in the heart of Amsterdam’s Zuidas district, a truly inspiring environment for teaching and research.

Diversity
We are an inclusive university community. Diversity is one of our most important values. We believe that engaging in international activities and welcoming students and staff from a wide variety of backgrounds enhances the quality of our education and research. We are always looking for people who can enrich our world with their own unique perspectives and experiences.

The Faculty of Science
The Faculty of Science inspires researchers and students to find sustainable solutions for complex societal issues. From forest fires to big data, from obesity to medicines and from molecules to the moon: our teaching and research programmes cover the full spectrum of the natural sciences. We share knowledge and experience with leading research institutes and industries, both here in the Netherlands and abroad.

Working at the Faculty of Science means working with students, PhD candidates and researchers, all with a clear focus on their field and a broad view of the world. We employ more than 1,250 staff members, and we are home to around 6,000 students.

About the department, institute, project
The VU Department of Computer Science has approximately 170 members, including 35 tenured staff members and 40-50 PhD students. The tenured staff members form the essential basis for the functioning of the department.

Are you interested in this position? Please apply via the application button and upload your curriculum vitae and cover letter until

Applications received by e-mail will not be processed.

Vacancy questions
If you have any questions regarding this vacancy, you may contact:

Name: Mark Hoogendoorn
Position: Professor of Artificial Intelligence / Chair Quantitative Data Analytics Group
E-mail: [email protected]

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