Postdoctoral Research Fellow (f/m/d) in Probabilistic Learning and Methods for Big Data the Kar[...]

The International Society for Bayesian Analysis; Eggenstein-Leopoldshafen; On-site; EUR 50,000 - 65,000; Full time

Benefits offered by this job

Extensive training opportunities

Flexible, family-friendly working hours

Job summary

A leading research institution invites applications for a Postdoctoral Research Fellow (f/m/d) position. The role focuses on developing innovative methods in statistical learning and involves theoretical investigation and implementation, requiring expertise in statistics and programming. Join a dynamic team with flexible working hours and extensive training opportunities. Applications are particularly encouraged from all genders, with a welcoming approach to female applicants and persons with disabilities.

Qualifications

• Completed PhD in statistics, mathematics, data science, or similar.
• Expertise in statistical learning, Bayesian methods, or machine learning.
• Proven record of publications in key research areas.

Responsibilities

• Develop methods leveraging statistical and Bayesian learning for big data.
• Investigate theoretical properties of developed algorithms.
• Publish results in leading journals and conferences.

Skills

Statistical Learning

Bayesian Deep Learning

Computational Skills

Analytical Skills

Programming in Python

Programming in R

Uncertainty Quantification

Education

PhD in Statistics

Master's in Statistics, Mathematics, or Data Science

Job description

Postdoctoral Research Fellow (f/m/d) in Probabilistic Learning and Methods for Big Data at the Karlsruhe Institute of Technology, Scientific Computing Center (SCC)

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:

1. Develop novel methods that leverage the potential of statistical learning and Bayesian (Deep) Learning.
2. Investigate the theoretical properties of the developed algorithms.
3. Implement the developed methods.
4. Contribute to joint research within the group.
5. Publish results in leading journals, workshops, and conferences.

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://www.scc.kit.edu/en/aboutus/working-at-scc.php.

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

Application deadline: 05.08.2024

Contact person: Prof. Dr. Nadja Klein, [e-mail]

Please apply online using the button below for vacancy number 340/2024. For personnel support, contact Mr. Meschar at [phone], 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.