PhD student in Machine Learning

We are looking for a new PhD student who will do research in the area of machine learning (ML) and in particular on reinforcement learning (RL) and the application of these techniques in a large health project (CAREPATH). The EU funded CAREPATH project focuses on improving medication adherence and persistence for patients with chronic conditions across six European countries. The position is embedded in the Quantitative Data Analytics (QDA) group of the VU in close collaboration with the Amsterdam University Medical Center and companies developing the interventions tools. The QDA group focuses on both fundamental and application-driven research in ML while the Amsterdam University Medical Center has a wealth of experience in applying complexity science to health intervention development. 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, the utilization of domain knowledge in ML, and data efficient ML methods. We are interested in attracting a PhD student that is able to perform ground-breaking research in fundamental aspects of machine learning and reinforcement learning, with a willingness to deploy the models in real-life applications.. The goal within the project is to apply the developed algorithms in an app and use them to personalize medication adherence interventions.

Your duties

• Perform research in machine learning and publish in top ML conferences
• Develop and deploy reinforcement learning algorithms for personalization in the CAREPATH app
• Work with multidisciplinary teams across the six pilot countries, including academic and industry partners
• Contribute to teaching activities and supervise Bachelor/Master students (20% of time)
• Help evaluate algorithm effectiveness in real-world healthcare settings

Requirements:

Requirements

• Master's degree in an area relevant to machine learning
• Strong programming skills with experience in PyTorch
• Experience in machine learning, particularly reinforcement learning
• Familiarity with healthcare applications or willingness to learn healthcare data standards
• Ability to work both independently and collaboratively
• Excellent communication skills for this international consortium
• Enthusiasm for applying RL to drive meaningful health behavior change

As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe that diversity in all its complexity is invaluable for the quality of our teaching, research and service. We are always looking for talent with diverse backgrounds and experiences. This also means that we are committed to creating an inclusive community so that we can use diversity as an asset.

We realise that each individual brings a unique set of skills, expertise and mindset. Therefore we are happy to invite anyone who recognises themselves in the profile to apply, even if you do not meet all the requirements.

Salary Benefits:

A challenging position in a socially engaged organisation. At VU Amsterdam, you contribute to education, research and service for a better world. And that is valuable. So in return for your efforts, we offer you:

• a salary of € 2.901,00 (PhD) and maximum € 3.707,00 (PhD) gross per month in the fourth year, for a full-time employment
• an employment contract of initially 18 months. If there is sufficient perspective, this will be extended to a total of 4 years. Your dissertation at the end of the fourth year forms the end of your employment contract.
• The position is vacant for at least 0.8 FTE.

We also offer you attractive fringe benefits and arrangements. Some examples:

• A full-time 38-hour working week comes with a holiday leave entitlement of 232 hours per year. If you choose to work 40 hours, you have 96 extra holiday leave hours on an annual basis. For part-timers, this is calculated pro rata.
• 8% holiday allowance and 8.3% end-of-year bonus
• solid pension scheme (ABP)
• contribution to commuting expenses
• optional model for designing a personalized benefits package

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