Benedikt Soja (0000-0002-7010-2147)

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Education:
Ph.D. study “Geodesy and Geoinformation”, Vienna University of Technology, Austria, completed with distinction and approved for “Promotio sub auspiciis Praesidentis rei publicae”, Ph.D. thesis: “Application of Kalman Filtering in VLBI Data Analysis”, supervisor: Prof. Dr. Harald Schuh (04/2015 – 04/2016)
Ph.D. study “Geodesy and Geoinformation Science”, Technische Universität Berlin, Germany (05/2013 – 03/2016)
Master study “Geodesy and Geophysics”, TU Vienna, “Diplom-Ingenieur” degree (MSc) with distinction, master thesis: “Probing the solar corona with VLBI”, supervisor: Prof. Dr. Johannes Böhm (07/2011 – 04/2013)
Semester abroad at ETH Zurich, Switzerland, study program “Geomatics” (02–08/2012)
Bachelor study “Geodesy and Geoinformation”, TU Vienna, “Bachelor of Science” degree, with distinction (10/2008 – 07/2011)

Employment:
Assistant Professor (Tenure Track) of Space Geodesy, ETH Zurich, Switzerland, Institute of Geodesy and Photogrammetry (sine 04/2020)
Scientist II at NASA Jet Propulsion Laboratory, Pasadena, California, USA, Group 335N: Geodynamics and Space Geodesy (07/2019 – 03/2020)
Postdoctoral researcher at NASA Jet Propulsion Laboratory, Pasadena, California, USA, Group 335N: Geodynamics and Space Geodesy (11/2016 – 07/2019)
Postdoctoral researcher at GFZ German Research Centre for Geosciences, Potsdam, Germany, Section 1.1: Space Geodetic Techniques (05/2016 – 10/2016)
Project assistant in project “VLBI analysis in real-time” (Austrian Science Fund), GFZ Potsdam, Section 1.1: Space Geodetic Techniques (05/2013 – 04/2016)

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HDTM: A Novel Model Providing Hydrostatic Delay and Weighted Mean Temperature for Real-Time GNSS Precipitable Water Vapor Retrieval

IEEE Transactions on Geoscience and Remote Sensing

2025 | Journal article

DOI: 10.1109/TGRS.2024.3519428

Contributors: Luohong Li; Hongxing Zhang; Yunbin Yuan; Matthias Aichinger-Rosenberger; Benedikt Soja

Benedikt Soja

https://orcid.org/0000-0002-7010-2147

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Works (50 of 109)

HDTM: A Novel Model Providing Hydrostatic Delay and Weighted Mean Temperature for Real-Time GNSS Precipitable Water Vapor Retrieval

Optimal Placement of VGOS Telescope in India: Simulation Insights for 24-Hour and 1-Hour VLBI Sessions

The role of climate change on Earth&#8217;s polar motion and the length of day &#160;

Climate‐Induced Polar Motion: 1900–2100

Downscaling GRACE-derived ocean bottom pressure anomalies using self-supervised data fusion

Laplacian deep ensembles: Methodology and application in predicting dUT1 considering geophysical fluids

AIRETH 2.0 &#8211; a revamped helicopter-borne GPR for glaciological applications

An Ionospheric Forecasting Model Based on Transfer Learning Using High-Resolution Global Ionospheric Maps

Explaining GNSS station movements based on Earth observation data

GGOS: Ensuring a Coherent Earth Observation System

Improving the spatial resolution of global mass changes observed by GRACE(-FO) using deep learning &#8212; from terrestrial water to the ocean

Machine learning-based regional slant ionospheric delay model and its application for PPP-RTK

Non-line-of-sight GNSS Signal Classification for Urban Navigation Using Machine Learning&#160;

The excitation of free core nutation: a reappraisal

Using Machine Learning for identifying TEC signatures related to earthquakes and tsunamis: the 2015 Illapel event case study

A New Deep-Learning-Assisted Global Water Vapor Stratification Model for GNSS Meteorology: Validations and Applications

Forecasting of Tropospheric Delay Using AI Foundation Models in Support of Microwave Remote Sensing

Operational Forecasting of Effective Angular Momentum Functions Fourteen Days Ahead

ZWDX: a global zenith wet delay forecasting model using XGBoost

Length of Day Variations Explained in a Bayesian Framework

Modelling and Analysing GNSS Displacements with Machine Learning and Environmental Variables

Revisiting the Excitation of Free Core Nutation

High-resolution Greenland ice sheet mass anomalies from data fusion using graph neural networks

Contributions of core, mantle and climatological processes to Earth’s polar motion

Machine learning-based detection of TEC signatures related to earthquakes and tsunamis: the 2015 Illapel case study

On the real-time tropospheric delay estimates using low-cost GNSS receivers and antennas

The increasingly dominant role of climate change on length of day variations

Global, spatially explicit modelling of zenith wet delay with XGBoost

Smartphone-based Vision/MEMS-IMU/GNSS tightly coupled seamless positioning using factor graph optimization

Uncertainties of Interpolating Satellite-Specific Slant Ionospheric Delays and Impacts on PPP-RTK

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Analyzing the Ionospheric Irregularities Caused by the September 2017 Geomagnetic Storm Using Ground-Based GNSS, Swarm, and FORMOSAT-3/COSMIC Data near the Equatorial Ionization Anomaly in East Africa

Modeling the Differences between Ultra-Rapid and Final Orbit Products of GPS Satellites Using Machine-Learning Approaches

Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

Geophysically Informed Machine Learning for Improving Rapid Estimation and Short‐Term Prediction of Earth Orientation Parameters

On the importance of accurate pole and station coordinates for VLBI Intensive baselines

Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project

Estimation of tropospheric parameters with GNSS smartphones in a differential approach

MPG-NET: A low-cost, multi-purpose GNSS co-location station network for environmental monitoring

GPS-SNR-based detection of severe weather events: two case studies of summer 2021 in Switzerland

Ultra-short-term prediction of LOD using LSTM neural networks

Active mitigation of spaceborne radio frequency interference for VLBI

Land motion in Europe imaged by GNSS

Troposphere Monitoring Based on Crowdsourced Smartphone GNSS Data

ResLearner: geophysically-informed machine learning for improving the accuracy of rapid Earth orientation parameters

High-resolution tropospheric refractivity fields by combining machine learning and collocation methods to correct earth observation data

Bridging astronomical, astrometric and geodetic scheduling for VGOS

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Peer review (3 reviews for 2 publications/grants)

The role of climate change on Earth’s polar motion and the length of day

AIRETH 2.0 – a revamped helicopter-borne GPR for glaciological applications

Improving the spatial resolution of global mass changes observed by GRACE(-FO) using deep learning — from terrestrial water to the ocean

Non-line-of-sight GNSS Signal Classification for Urban Navigation Using Machine Learning