Postdoctoral Research Fellow; f​/m​/d in Probabilistic Learning and Methods Big Data Kar

The Scientific Computing Center is the Information Technology Center of KIT. The newly established Research Group Methods for Big Data, starting in August 2024, welcomes applications for a postdoctoral research fellow (f/m/d).

In this project, you will:

Job requirements:

• Excellent university degree (master's) and completed PhD in statistics, mathematics, data science, computer science, or similar fields.
• Expertise in at least one of the following areas:
  Statistical Learning, Bayesian (Deep) Learning, Uncertainty Quantification and Probabilistic Machine Learning, High-Dimensional Statistics, Distributional Regression, Causal Inference, Continual Learning, Hybrid Algorithms.
• Proven track record and excellent publications in at least one of these main research areas.
• Strong computational and mathematical skills.
• Solid programming skills in scientific languages such as Python or R.
• High proficiency in English, both written and spoken.
• Creativity, commitment, analytical skills, and ability to work interdisciplinary.

We offer an exciting, varied job within an agile team, with extensive training opportunities and flexible, family-friendly working hours. For more information about SCC, visit https://(Sie können sich bewerben oder uns per E-Mail kontaktieren in dem Sie die untenstehende Online-Bewerbungsbox verwenden).

We look forward to your application (motivational letter, CV, certificates).

Salary category 13, depending on professional and personal qualifications.

Organizational unit: Scientific Computing Center (SCC)

Start date: ASAP

Duration: 3 years

Final date to receive applications: 05.08.2024

Contact person: Prof. Dr. Nadja Klein,

Please apply online using the button below for vacancy number 340/2024. For personnel support, contact Mr. Meschar at , Hermann-van-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.

We encourage applications from all genders (f/m/d). Female applicants are especially welcome. Recognized severely disabled persons will be preferred if equally qualified.