PhD or Postdoc position on Probabilistic Modeling in the Wild
Real world environments are often noisy, ambiguous, evolving, and subject to contamination. In turn, deploying intelligent systems in these environments often …
- Science Park, Amsterdam, Noord-Holland
- Vast contract
- Uren per week:
- 38 - 38 uur
- € 2395 - € 4402 per maand
Real world environments are often noisy, ambiguous, evolving, and subject to contamination. In turn, deploying intelligent systems in these environments often requires a probabilistic approach. Quantifying uncertainty can help the system handle the ‘messiness’ of the real world. The Amsterdam Machine Learning Lab is looking for a PhD student or a postdoctoral researcher to study principled methods for deploying probabilistic models in the wild. Examples of avenues of research include (but are not limited to): how to incorporate prior knowledge and constraints, how to learn under dynamic computational limitations, how to ensure the system is robust to data shift, and how to efficiently incorporate human oversight. Such issues are of crucial importance when building probabilistic systems in practice. The solutions will require deep engagement with both statistical methodology (e.g. Bayesian modeling) and engineering practice (e.g. probabilistic programming).
What are you going to do?
- Invent and evaluate novel methodologies for probabilistic modeling under challenging settings;
- show that these methods are useful in realistic scenarios, for instance, in decision support systems for healthcare;
- present your research by contributing to international conferences, workshops, and journals;
- actively participate in the Amsterdam Machine Learning Lab and other local research communities;
- assist in teaching activities: teaching labs and tutorials or supervising bachelor, master, and for postdocs, PhD students;
- for PhD applicants: complete a PhD thesis within the duration of four years.
What do we require?
- For PhD applicants: Master’s degree in Machine Learning, Statistics, Computer Science, Mathematics, or a related field;
- for postdoc applicants: PhD in Machine Learning, Statistics, Computer Science, Mathematics, or a related field;
- experience in programming and software development. Familiarity with Python and scientific computing libraries (e.g. NumPy, Stan, TensorFlow, PyTorch) is preferred;
- enthusiasm for the scientific process: formulating and conducting experiments, data collection and analysis, disseminating findings via writing and oral presentations;
- ability to cooperate and work effectively within a team;
- fluency in English, both written and spoken.
For a PhD position:
A temporary contract for 38 hours per week for the duration of 4 years (initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.
The salary will be €2,395 to €3,061 (scale P) gross per month, based on a full-time contract of 38 hours a week.
For a postdoc position:
A temporary contract for 38 hours per week for the duration of 12 months. After positive evaluation, the contract will be extended with 24 months. The salary, depending on relevant experience before the beginning of the employment contract, will be €2,790 to €4,402 (scale 10) gross per month, based on a full-time contract of 38 hours a week.
The salary is exclusive 8 % holiday allowance and 8,3% end-of-year bonus. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities is applicable.
Are you curious about our extensive package of secondary employment benefits like our excellent opportunities for study and development? Then find out more about working at the Faculty of Science.
Do you have questions about this vacancy? Or do you want to know more about our organisation? Please contact:
- dr Eric Nalisnick, assistant professor