PhD – Meta Learning from Few Tasks with Hybrid Models
- Robert-Bosch-Campus 1, 71272 Renningen, Germany
- Legal Entity: Robert Bosch GmbH
Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology – with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.
The Robert Bosch GmbH is looking forward to your application!
Single-task learning aims to generalize over a sample set collected for a specific learning task (e.g. 10-class object classification from images). Contrarily, meta-learning assumes few examples available for progressively many tasks and aims to learn the regularities that govern the occurrence of these tasks. Meta-learning aims to infer a distribution on tasks, so that a sensible learner can be sampled for a new task for which only a handful of observations are available. Hence, they directly target extrapolation settings, which call for a proper uncertainty treatment. This PhD position is about development of novel meta-learning methods that benefit maximally from well-calibrated uncertainties provided from Bayesian task predictors. During your PhD you will be part of the active research team at the Bosch Center for Artificial Intelligence (BCAI, www.bosch-ai.com).
- Help shape the future: You invent novel ways to integrate Bayesian neural networks into the meta-learning setup.
- Observe, and think ahead: You conduct prototypical algorithm implementations and benchmarking on real-world data sets.
- Take responsibility: You publish in top-tier conferences (ICML, NIPS, ICLR, AISTATS, etc.) and journals (JMLR, PAMI, etc.).
- Experience cooperation: You screen literature and build up close contact with the academic community.
- Networked communication: You participate in academic interactions within the BCAI research team.
- Personality: enthusiastic for science, excited to tackle theoretical challenges, team player
- Working Practice: perform exclusively academic research with excellence (i.e. no industry project duties), gradually develop scientific independence until graduation, make significant contributions to science
- Experience and Knowledge: general knowledge of machine learning, excellent theoretical skills proven by top course grades, prior experience on scientific writing is a plus, a background on Gaussian processes or Bayesian inference is preferable
- Enthusiasm: intrinsic motivation for top-level research and scientific independence
- Languages: fluent in English (written and spoken)
- Education: master degree in machine learning, mathematics, statistics, physics, computer science, or related fields with excellent grades
The final PhD topic is subject to your university.
Duration: 3 years
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223
Need further information about the job?
Melih Kandemir (Functional Department)
+49 711 811-52789