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Paid Internship
Work Mode
Time Spent
Required Degree
Duration

24Open Positions

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Working Student: Soil & Crop Modelling

klimtechnologies
Found 1 week ago
Location
Berlin, Germany
Time
Part-time
Work Mode
Hybrid
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
1 week ago

Education

  • Bachelor
  • Master

Skills & Qualifications

Technical Skills

  • Python
  • tabular datasets
  • geospatial datasets

Soft Skills

  • Detail-oriented
  • strong organisational skills
  • document work clearly
  • Good written and spoken English

Job Description

Data Processing and Management Process and quality-check soil sampling datasets (TOC, bulk density, GPS coordinates, sample metadata) received from soil sampling partners Integrate soil sampling data into stratification workflows and model input pipelines Maintain data traceability and documentation standards aligned with Klim’s reproducibility requirements Model Support Support the preparation and formatting of RothC model inputs (crop parameterisation, climate data, organic matter inputs) Assist with calibration and validation (Cal/Val) workflows, including dataset splitting, statistics calculation, and results formatting Contribute to sensitivity analysis and uncertainty calculations in line with VMD0053 v2.1 requirements Scientific Analysis and Documentation Help produce figures, tables, and written summaries of model results for internal and external reporting Contribute to the preparation of documentation for the Independent Modelling Expert (IME) and SustainCERT verification processes Review and summarise relevant scientific literature on soil carbon dynamics, crop parameterisation, and calibration methodologies Ad Hoc Support Support the team with scripting, data visualisation, and exploratory data analysis tasks as required. Contribute to the development of internal tools, dashboards, or automation scripts

Requirements

  • Currently enrolled in a relevant degree programme (e.g. agricultural science, environmental science, data science, geosciences, soil science, applied mathematics)
  • Towards the end of your studies (final year or master’s level preferred), but with at least one year remaining on your degree programme
  • Proficient in Python for data processing, scripting, and analysis — this is a firm requirement for the role
  • Comfortable working with tabular and geospatial datasets
  • Detail-oriented with strong organisational skills and the ability to document work clearly
  • Good written and spoken English; German is a plus

Related Field

  • AI & Machine Learning

Related Subfield

  • Applied Machine Learning

Languages

  • English

Nice to Haves

  • Previous professional experience (internship or working student role) in a relevant field
  • Familiarity with soil carbon models (RothC, CENTURY, etc.) or biogeochemical modelling concepts
  • Experience with GIS tools, R, or SQL
  • Understanding of carbon accounting standards (e.g. Verra VCS, ISO 14064) is a plus but not required
  • Knowledge of agricultural practices, soil science, or agronomy
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