Ph.D. Position in Machine Learning: A unique Ph.D. opportunity is available for highly motivated candidates with a strong background in applied mathematics, optimization, or machine learning. This joint program, supervised by leading researchers from TU Darmstadt and KTH Stockholm, focuses on cutting-edge research at the intersection of Optimal Transport (OT), Machine Learning, Bayesian Inference, Gradient Flows, and Control Theory. The selected candidate will have the chance to collaborate across institutions and work on foundational and applied topics in computational and theoretical ML.
Ph.D. Position in Optimal Transport and Machine Learning
Joint Program between TU Darmstadt (Germany) and KTH Stockholm (Sweden)
🔍 Quick Summary Table
| Field | Details |
|---|---|
| Position Title | Ph.D. Student |
| Research Areas | Optimal Transport, Machine Learning, Optimization, Control, PDE/SDE |
| Institutions | TU Darmstadt (Germany), KTH Stockholm (Sweden) |
| Supervisors | Jia-Jie Zhu (KTH Stockholm), Jan Peters (TU Darmstadt) |
| Location | Primarily TU Darmstadt, with visits to KTH Stockholm |
| Eligibility | Master’s in Applied Math/ML or related fields |
| Start Date | Open until filled |
| Application Email | zplusj@gmail.com (Subject: [apply-phd-TUDA-KTH]) |
| Official Application Portal | Apply Here |
📚 Designation
Ph.D. Researcher
🧠 Research Areas
- Theory and computational aspects of Optimal Transport (OT)
- Gradient Flows, PDEs/SDEs in Machine Learning
- Deep Generative Models and Kernel Methods
- Bayesian Inference and Probabilistic ML
- Distributionally Robust Optimization and Control
- Optimal Transport for Reinforcement Learning and Robotics
📍 Location
- Primary Host: Technical University of Darmstadt, Germany
- Collaborating Institution: KTH Royal Institute of Technology, Stockholm, Sweden
- International visits and collaboration opportunities included
✅ Eligibility/Qualification
Candidates should have:
- A Master’s degree in Applied Mathematics, Machine Learning, or closely related fields.
- Solid understanding and research experience in at least one of the following:
- Optimal transport, gradient flows, PDE/SDE in ML context
- Deep generative models, Bayesian inference
- Kernel methods, interacting particle systems
- Robust optimization and control theory
- Evidence of research ability through technical reports, Master’s thesis, or publications in top ML/AI conferences (e.g., NeurIPS, ICML, ICLR, AISTATS, UAI, AAAI).
📝 Job Description
The Ph.D. candidate will:
- Conduct original research in the intersection of optimal transport and machine learning.
- Work jointly with two renowned labs:
- KTH Stockholm (focus: optimization, applied math)
- TU Darmstadt (focus: reinforcement learning, robotics/control)
- Collaborate internationally and participate in exchange visits between institutions.
- Contribute to publications in top-tier journals and conferences.
📨 How to Apply
Interested and qualified candidates should send the following documents via email to zplusj@gmail.com with the subject line:
[apply-phd-TUDA-KTH]
- CV
- Master’s thesis (if available)
- Technical reports or relevant publications
- Complete transcripts (Bachelor’s and Master’s)
Note: Only candidates whose profiles align with the research focus will be contacted due to the volume of inquiries.
🧾 Formal applications, including two recommendation letters, must also be submitted through the official application portal:
👉 https://www.ias.informatik.tu-darmstadt.de/Jobs/Application
📅 Last Date to Apply
Open Until Filled
(Early application is encouraged as the position will be filled on a rolling basis.)
