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PhD. Position Continual learning and deep generative modeling for adaptive systems

At Vrije Universiteit Amsterdam, we are looking for an enthusiastic PhD candidate who is interested in formulating and developing new models and algorithms for quantifying uncertainty and making decisions in changing environments. Our project “Continual learning and deep generative modeling for adaptive systems’’ focuses on fundamental research into combining various learning paradigms for building intelligent systems capable of learning in a continuous manner and evaluating uncertainty of the surrounding environment. Interested? Apply at Vrije Universiteit Amsterdam.

3 maanden geleden


De Boelelaan, Amsterdam, Noord-Holland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
40 - 40 uur
€ 2325 - € 2972 per maand


Adaptivity is a crucial capability of living organisms. Current machine learning systems are not equipped with tools that allow them to adjust to new situations and understand their surroundings (e.g., observed data). For instance, a robot should be able to adapt to new environment or task and assess whether the observed reality is known (i.e., likely events) or it should contact a human operator due to unusual observations (i.e., high uncertainty). Moreover, we claim that uncertainty assessment is crucial for communicating with human beings and for decision making. In this project, we aim at designing new models and learning algorithms by combining multiple machine learning methods and developing new ones. In order to quantify uncertainties, we prefer to use deep generative modeling paradigm and frameworks like Variational Autoencoders and flow-based models. However, we believe that standard learning techniques are insufficient to update models and, therefore, continual learning (a.k.a. life-long learning, continuous learning) should be used. Since this is still an open question how continual learning ought to be formulated, we propose to explore different directions that could include, but are not limited to Bayesian nonparametrics and (Bayesian) model distillation. Moreover, a combination of continual learning and deep generative modeling entails new challenges and new research questions.


  • The prospective candidate has a Master’s degree or equivalent in AI, Computer Science, Mathematics, Statistics, Data Science or Physics
  • Candidates from other fields are welcome if they can prove their experience in machine learning/deep learning
  • A candidate should have a solid background in mathematics and programming (Python), demonstrated by successful completion of associated courses
  • A candidate should have keen interest in Artificial Intelligence


A challenging position in a socially involved organization. The salary will be in accordance with university regulations for academic personnel and amounts €2,325 per month during the first year and increases to €2,972 per month during the fourth year, based on a full-time employment. The job profile of promovendus: is based on the university job ranking system and is vacant for at least 1 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
  • solid pension scheme (ABP), contribution to commuting expenses
  • optional model for designing a personalized benefits package

Additional information

Are you interested in this position? Please apply via the application button and upload your curriculum vitae and cover letter until 15 April 2020.

Applications received by e-mail will not be processed.

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

Name: Dr. Jakub Tomczak
Position: Assistant Professor
E-mail: j.m.tomczak@vu.nl
Telephone: +31 (0)20 59 87772

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