PHD - machine learning and game theory

Bosch packaging technology/osgood industries, inc.

• Renningen

neues Angebot (05/03/2020)

stellenbeschreibung

Job Description Company Description 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! Job Description A combination of machine learning and multi-agent aspects lies at the core of a wide range of methods and theories that aim to address major societal challenges:

from highly automated driving over shared mobility, urban congestion pricing and decentralized power generation to foundations in data-driven game-theoretic mechanism design. The goal of this PhD thesis is to distill promising and general research questions from these areas, and answer them:

• Help shape the future:

You develop new models (model classes with good inductive biases for given tasks), algorithms, and/or theory (prove mathematical theorems etc

). * Create Something New

:

topics you could work on include, but are not limited to:
machine learning algorithms that forecast demand (e. g., time series analysis and recurrent neural nets on urban traffic data);
extension to data-driven game-theoretic models that also incorporate/reveal agents' preferences;
transfer learning combined with game theory and mechanism design for decision making tasks (e. g. congestion pricing);
adversarially-robust machine learning;

and proving theorems that provide insights/guarantees about the aforementioned problems/models/algorithms. * integrated implementation:

code and evaluate your algorithms on relevant data sets and tasks. * networked communication:
you publish papers at top-tier conferences (nips, icml, aamas, uai, etc. ) and/or journals (jmlr etc. ), develop a substantial understanding of the relevant existing work and keep close contact with the academic community. * experience cooperation:

benefit from being part of a leading ai industry research lab, the bosch center for artificial intelligence (bcai). participate in academic interactions within the bcai research team and perform exclusively academic research with excellence (i. e. no industry project duties). qualifications * education:

excellent degree (master) in mathematics, computer science, physics or similar * personality:

good communication and team work skills * working practice:

structured, independently and inquisitive * experience and knowledge:
very good math skills, good coding skills, familiar with machine learning;

ideally:

background in game theory and/or economics and prior experience in scientific writing * enthusiasm:
a strong passion for doing top-level research, and a genuine interest in multi-agent/economic/social systems and how they can be improved using machine learning * languages:

very good in english written and spoken additional information the following publication is an example of our research in this direction:
please submit all relevant documents (incl. curriculum vitae, cover letter, certificates). 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? philipp geiger (functional department) +49 711 811 92277 qualifications:

education:

excellent degree (master) in mathematics, computer science, physics or similar personality:

good communication and team work skills working practice:

structured, independently and inquisitive experience and knowledge:
very good math skills, good coding skills, familiar with machine learning;

ideally:

background in game theory and/or economics and prior experience in scientific writing enthusiasm:
a strong passion for doing top-level research, and a genuine interest in multi-agent/economic/social systems and how they can be improved using machine learning languages:

very good in english written and spoken responsibilities:
a combination of machine learning and multi-agent aspects lies at the core of a wide range of methods and theories that aim to address major societal challenges:

from highly automated driving over shared mobility, urban congestion pricing and decentralized power generation to foundations in data-driven game-theoretic mechanism design. the goal of this phd thesis is to distill promising and general research questions from these areas, and answer them:

help shape the future:
you develop new models (model classes with good inductive biases for given tasks), algorithms, and/or theory (prove mathematical theorems etc. ). create something new:

topics you could work on include, but are not limited to:
machine learning algorithms that forecast demand (e. g., time series analysis and recurrent neural nets on urban traffic data);
extension to data-driven game-theoretic models that also incorporate/reveal agents' preferences;
transfer learning combined with game theory and mechanism design for decision making tasks (e. g. conge

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