Postdoc: Probabilistic ML for Energy Materials Design

DTU — Technical University of Denmark; Kongens Lyngby; Vor Ort; DKK 420.000 - 540.000; Vollzeit

Zusammenfassung

DTU Compute is seeking a highly motivated postdoctoral researcher to develop probabilistic modeling and active learning methods for next-generation solar energy materials. The role involves bridging synthesis data with material performance, and you will collaborate with the Thin-Film Photovoltaics Group to guide experiments.

Candidates should have a PhD and a strong publication record in probabilistic ML or related fields, with experience in Python and uncertainty quantification.

Qualifikationen

• PhD required or equivalent
• Strong background in probabilistic ML and uncertainty quantification
• Experience with ML methods in Python for scientific computing
• Publication record in ML or related fields
• Excellent written and spoken English

Aufgaben

• Develop probabilistic models linking synthesis conditions with material properties and photovoltaic performance.
• Research active learning and Bayesian optimization for informative experiments under constraints.
• Integrate physical and chemical knowledge into models and identify descriptors affecting defects and bandgap.
• Develop multi-objective strategies for targeting bandgaps and reducing recombination losses.
• Collaborate with experimental postdoc and DTU Electro researchers to design experiments and interpret results.
• Publish papers and present results at ML conferences and interdisciplinary venues.
• Contribute to teaching and supervision of PhD and MSc/BSc students.

Kenntnisse

Probabilistic ML

Gaussian processes

Bayesian neural networks

Uncertainty quantification

Active learning

Bayesian optimization

Python

Ausbildung

PhD in a relevant field

Tools

Python (NumPy/SciPy)

PyTorch / TensorFlow

Jobbeschreibung

DTU Compute is seeking a highly motivated postdoctoral researcher to develop probabilistic modeling and active learning methods for next-generation solar energy materials. The role involves bridging synthesis data with material performance, and you will collaborate with the Thin-Film Photovoltaics Group to guide experiments.

Candidates should have a PhD and a strong publication record in probabilistic ML or related fields, with experience in Python and uncertainty quantification.