IAESTE Internships

Location

Austria

Internship type

ON-SITE

Reference number

AT-2024-2015GR

Students Requirements

General discipline

CIVIL ENGINEERING
GEOLOGY AND MINING

Completed Years of Study

3

Fields of Study

Geotechnical and Geoenvironmental Engineering
Mining and Mineral Engineering
Geological/Geophysical Engineering

Languages

English Good (B1, B2)

Required Knowledge and Experience

-

Other Requirements

Scientific writing skills; data organization/structuring skills; attention to detail

Work Details

Duration

12 - 24 Weeks

Within These Dates

01.10.2024 - 30.06.2025

Holidays

NONE

Work Environment

-

Gross pay

1200 EUR / month

Working Hours

40.0 per week / 8.0 per day

Living Lodging

Type of Accommoditation

Trainee with the help of IAESTE

Cost of lodging

350 EUR / month

Cost of living

700 EUR / month

Work Offered

Additional Info

We expect an internship in return from the country of the selected student.

Work description

We are looking for a suitable candidate in one of our two available projects:Project 1: Assistance in the MLGT (machine learning in geotechnics workgroup), which explorespossible applications of machine learning for geotechnical and engineering geological problems. Goalsinclude increasing the overall utilization of geo-data, developing workflows that permit efficient data-handling and using machine learning as decision support tools.Current fields of research include: Improving geological predictions in tunnelling, Anomaly detection ingeotechnical datasets, Synthetic data generation, Measurement while drilling data analysis, Digitalisationof archived geotechnical reports.The trainee will actively contribute to one or more of the above mentioned research fields through coding, discussions, report writing and literature research.Project 2: In this research project, we work on the numerical simulation of transitional stratum termedHard Soil/Soft Rock (HSSR). Since it is geomechanically challenging, it is also difficult to model withexisting numerical methods. The goal is to use in-situ as well as laboratory data to accurately model aclosely monitored and studied lithology from a tunnelling site in Austria and thus, participate in a detailedmaterial characterization of an often overlooked geology.

Deadline

22.05.2024