PhD Student position
vor 2 Monaten
Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin
The Weierstrass Institute for Applied Analysis and Stochastics (WIAS) is an institute of the Forschungsverbund Berlin e.V. (FVB). The FVB comprises seven non-university research institutes in Berlin which are funded by the federal and state governments. The research institutes belong to the Leibniz Association.
WIAS invites applications for a PhD student position (f/m/d) (Ref. 24/12)
in the Research Group
"Stochastic algorithms and nonparametric statistics"
to be filled at October 1st, 2024, or the earliest possible date thereafter. The position is associated with the SFB/Transregio 388.
SFB/Transregio 388 investigates the interplay between rough analysis and stochastic dynamics. Central aspects include rough paths and subsequent developments for nonlinear stochastic partial differential equations. The theory of signatures and rough volatility also provides important connections to algebra, statistics, and financial mathematics.
Job description:
Rough volatility models are an important class of equity models in mathematical finance. Due to the lack of Markov and semi-martingale properties, proper analysis and numerics for rough volatility models is challenging. Project B02 within the SFB/Transregio 388 is concerned with microstructural foundations of rough volatility models, using techniques from stochastic analysis, rough paths, and regularity structures. Specific tasks include:
- Analysis of the (expected) signature of Hawkes and other pure jump processes and their cumulants.
- Derivation of scaling limits for such processes to rough volatility models.
- Extensions to the setting of regularity structures for rough volatility.
The position is hosted in Research Group "Stochastic algorithms and nonparametric statistics" at WIAS, and is a collaboration between Ch. Bayer (WIAS), U. Horst (HU Berlin), and D. Kreher (HU Berlin). It includes the possibility for a doctorate.
We are looking for: candidates with a master's degree in mathematics or a closely related field with a strong background in stochastic analysis. Prior knowledge in rough paths and financial mathematics is beneficial.
What we offer:
- Close mentorship: the PhD student will receive responsible and careful mentorship. We emphasize fostering a healthy mentor-mentee relationship.
- WIAS Berlin is a premier research institution known for its strength in rough volatility, stochastic numerics, and applied mathematics in general.
- SFB/Transregio 388 is an excellent centre for rough and stochastic analysis, providing many opportunities for interactions with renown expert in the field, as well as with highly motivated fellow students.
- A certified (Audit berufundfamilie) family-friendly work environment.
- Berlin is one of the most culture-rich and diverse international cities in the world. It offers endless opportunities to enjoy life outside work, while being very affordable compared to other major cities. Neither the job nor living in Berlin requires German language (although WIAS offers free German courses). We highly welcome international applications. Scientifically, Berlin offers a rich landscape with numerous opportunities for research, as well as job prospects in academia and industry.
Please direct scientific queries to Ch. Bayerwias-).
The appointment is limited to 3.75 years. The reduced work schedule is 29,25 hours per week, and the salary is according to the German TVoeD Bund scale.
The Weierstraß Institute is an equal opportunity employer. We explicitly encourage female researchers to apply for the offered position. Among equally qualified applicants, disabled candidates will be given preference.
How to apply:
Please upload your complete application documents (motivation letter, detailed CV, certificates, list of MSc courses and grades, copy of the master thesis, reference letters, etc.) via our applicant portal using the button "Apply online".
The advertisement is open with immediate effect and will remain open until the position
will be filled.
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