PhD Student
vor 3 Wochen
Job description
In this PhD project, you will join an interdisciplinary team working on machine learning under uncertainties. Your goal is to find novel solutions for approximating probability density functions (pdf) based on information measures such as entropy and Fisher information. A special focus is on deterministic sampling, i.e., approximating a continuous pdf by a discrete one. Depending on your preferences, a suitable balance between theoretical development and practical applications will be found.
The final goal of your scientific activities at KIT is your doctorate.
Starting date
as soon as possible
Personal qualification
University Master’s degree in STEM areas and strong English communication skills (both oral and in writing). Basic knowledge and scientific interest in probability and stochastic systems. Strong willingness for independent research, self-learning, creativity, and teamwork.
Salary
Salary category 13 TV-L, depending on the fulfillment of professional and personal requirements.
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