IAESTE Internships

Location

Switzerland

Internship type

ON-SITE

Reference number

CH-2024-000092

Students Requirements

General discipline

NATURAL RESOURCES AND CONSERVATION
MECHANICAL ENGINEERING
CHEMISTRY AND CHEMICAL ENGINEERING

Completed Years of Study

3

Fields of Study

Languages

English Good (B1, B2)

Required Knowledge and Experience

-

Other Requirements

Fluent in English, written and spoken (min B2); Interview required

Work Details

Duration

16 - 52 Weeks

Within These Dates

01.06.2024 - 31.08.2025

Holidays

NONE

Work Environment

-

Gross pay

2000 CHF / month

Working Hours

42.0 per week / 8.4 per day

Living Lodging

Type of Accommoditation

Employer

Cost of lodging

810 CHF / month

Cost of living

1600 CHF / month

Work Offered

Additional Info

Students with any NON-EU/EFTA nationality need for the visa and work permit an official letter from their university, confirming that the internship is compulsory (IAESTE Switzerland will apply for them).

Work description

Research scope:Recent research reveals that the potent greenhouse gas (GHG) and ozone depleting substance nitrous oxide (N2O) dominates GHG emissions from biological wastewater treatment. The development of effective operational strategies to reduce or avoid N2O generation requires in-depth understanding of the N2O formation mechanisms. This internship involves advanced data analysis using machine-learning (ML) techniques on long-term datasets from full-scale biological wastewater treatment plants, with the goal of unravelling hidden relationships between operating conditions and N2O emissions.Work plan:Starting material: Clear overview of the existing data and its preprocessing a specific WWTPFollowing tasks will be carried out during the internship (20 weeks – longer or shorter period is possible (min. 16 weeks)): 1. Getting familiar with the available data (2 weeks)2. Screening analysis of ML models (4 weeks)TASKS: (1) Creation of benchmark model (able to predict average N2O emissions), (2) generation of different ML models, (3) benchmarking based on predictive accuracy, (4) keep two most promising models.OUTPUT: Insights whether the dataset provides enough information for the estimation of N2O formation.3. Optimization and training two best ML models (4 weeks)TASKS: Optimization and training of the two most promising ML model.OUTPUT: Two ML models which quantitatively represent the N2O emissions.4. First insights in explainable machine learning (8 weeks)TASKS: (1) Review of XAI tools and possibilities (2) Employ XAI tools: understand predictions of the ML models and if possible translate into mechanistic building blocksOUTPUT: First insights in using ML to support mechanistic N2O model building.5. Reporting and buffer (2 weeks)You will be supervised on a daily basis by a postdoctoral student. A workplace with a computer will be provided in Dübendorf.

Deadline

05.05.2024