Postdoc: Scalable Imaging Data Reduction & Compression

Lund University; Lunds kommun; Sur place; SEK 550 000 - 750 000; Plein temps

Résumé du poste

The Scientific Data Group at MAX IV Laboratory, part of the Technical Division, seeks a Postdoctoral Researcher to advance data reduction and lossy-compression workflows for tomography and imaging.

The successful candidate will evaluate, develop and deploy advanced data reduction methods, collaborate with European photon science facilities, and contribute to reproducible workflows across large-scale imaging datasets and FAIR data practices.

Qualifications

• PhD in a closely related discipline.
• Experience in scientific programming or data-intensive computing.
• Strong experience with Python and at least one high-performance language (C++, Rust, or Julia).
• Experience with large-scale imaging datasets and tomography data analysis.

Responsabilités

• Evaluate and benchmark data reduction and lossy-compression methods using representative tomography and imaging datasets.
• Develop software tools, workflows and validation methodologies for adoption of compression technologies.
• Collaborate with beamline scientists to evaluate data reduction impact on outcomes and data quality.
• Contribute to community guidelines and best practices for scientific data reduction and FAIR data management.
• Work with researchers at European photon science facilities to facilitate knowledge exchange.
• Coordinate project workshops, training activities and dissemination events.
• Disseminate results through reports, publications, and presentations.

Connaissances

Python

C++

Rust

Julia

Scientific programming

Linux

Version control

English communication

Formation

PhD in Computer Science / Mathematics / Physics / Engineering / Scientific Computing / Image Processing / Data Science

Outils

HDF5

NeXus

Zarr

Description du poste

The Scientific Data Group at MAX IV Laboratory, part of the Technical Division, seeks a Postdoctoral Researcher to advance data reduction and lossy-compression workflows for tomography and imaging.

The successful candidate will evaluate, develop and deploy advanced data reduction methods, collaborate with European photon science facilities, and contribute to reproducible workflows across large-scale imaging datasets and FAIR data practices.