PRAEDOC DISS for Advanced Transformer-Based Geostatistical Simulation Research (F/M/X)
PRAEDOC DISS for Advanced Transformer-Based Geostatistical Simulation Research (F/M/X)
- Wien
- Vollzeit
PRAEDOC DISS for Advanced Transformer-Based Geostatistical Simulation Research (F/M/X)
Über diesen Job
The Austrian Academy of Sciences (OeAW), Austria’s leading non-university research and science institution, is offering a Position as
PRAEDOC DISS for Advanced Transformer-Based Geostatistical Simulation Research (F/M/X)
(Part-time employee / 30h per week)
This position is part of a FWF-funded project within the Digital Landscape group at the IGF, based in Innsbruck, Austria, and will involve collaboration with Grégoire Mariethoz from the University of Lausanne. The position is for a fixed term of 4 years with an expected start on 1st September 2025 (negotiable).
(Office & remote)
Your Tasks
- Develop and merge innovative approaches in spatial data encoding, continuous output representation, and multi-variable simulation with dynamic data integration (Retrieval Augmented Generation, RAG) to push the boundaries of spatial stochastic simulation research.
- Design, prototype, and test advanced transformer-based methods tailored to complex spatial and multi-variable data.
- Create robust training protocols to manage non-stationary data and develop strategies for continuous output (e.g., raw value predictions, Fourier decomposition).
- Implement tokenization and cross-attention techniques to efficiently handle multi-variable simulations.
- Collaborate with international partners and contribute to an environment that values scientific freedom, interdisciplinary work, and curiosity-driven exploration.
Your Profile
We recognize that maybe no single candidate will cover all qualifications; if you are passionate about the topic, please explain how your background can contribute.
- A Master’s degree (or equivalent) in computer science, machine learning, statistics, applied mathematics, or a closely related discipline. We are also open to atypical profiles if you can demonstrate in your cover letter how your background is relevant to the project.
- Strong programming skills with proficiency in TensorFlow or PyTorch.
- Ability to demonstrate competence through your Master’s thesis, academic record, and coding examples (e.g., GitHub link).
- Excellent analytical, problem-solving, and communication skills.
- Open-mindedness, genuine curiosity, out-of-the-box and critical thinking, ability to embrace interdisciplinary and non-conventional approaches.
- Prior experience in advanced simulation or stochastic modelling is a plus.
Our Offer
- Engaging Research Environment: Work in a dynamic, interdisciplinary setting that encourages scientific freedom and curiosity-driven research, collaborating with international experts.
- License to AI tools (e.g., ChatGPT)
- Work-Life Balance: Up to 40% remote work available.
- Location: Be part of the Digital Landscape group at IGF in Innsbruck, Austria—a city renowned for its exceptional mountain landscape and vibrant academic community.
- The position offers an annual gross salary of € 39.208,82 (based on the salary scheme of the ÖAW)
Please apply online including a letter of motivation, your CV, your Master’s grade sheet and thesis summary and a code example (e.g., a GitHub link) no later than 30 June 2025.
The Austrian Academy of Sciences (OeAW) pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity. Individuals from underrepresented groups are particularly encouraged to apply.
PRAEDOC DISS for Advanced Transformer-Based Geostatistical Simulation Research (F/M/X)
PRAEDOC DISS for Advanced Transformer-Based Geostatistical Simulation Research (F/M/X)
-
Innsbruck
- Vollzeit
Bewertung von Mitarbeitenden
Gesamtbewertung
Basierend auf 69 BewertungenVorteile für Mitarbeitende
Unternehmenskultur
Österreichische Akademie der Wissenschaften
Branchen-Durchschnitt
Unternehmenskultur
69 Mitarbeitende haben abgestimmt: Sie bewerten die Unternehmenskultur bei Österreichische Akademie der Wissenschaften als modern. Dies stimmt in etwa mit dem Branchen-Durchschnitt überein.